diff --git a/.gitignore b/.gitignore index 815c7e5..53a3097 100644 --- a/.gitignore +++ b/.gitignore @@ -1,130 +1,64 @@ +# macOS .DS_Store -sac_MSH/.DS_Store -Utilities_Matlab/.DS_Store -# MATLAB / Simulink --------------------------------------------------------- -# Temporary files and backups -*~ +# MATLAB/Simulink temporary, backup, and user files *.asv -*.bak -*.tmp *.autosave +*~ +*.bak +*.mat +*.fig +*.mltbx +*.mlsettings +.matlab/ +.history -# MATLAB specific -*.mlappinstall -*.mlappinstallinfo -*.mltbx # MATLAB toolbox archive (optionally track if you want) -*.mltbxmanifest -*.mldatx # Simulink data dictionary (if you want to ignore local copies) -*.mlpkginstall -*.prj # generic project files (ignore if you keep projects in repo) -*.mlproj -matlab.prf # MATLAB preferences -*.mlpkg -*.mlapp # App Designer saved files (keep or ignore depending on policy) +# MATLAB build / generated code / caches +slprj/ +codegen/ +rtw/ +build/ +*_ert_rtw/ +*/.slx-cache/ +*.slxc +*.slxautosave -# Compiled and binary artifacts +# Compiled / binary / object files *.mex* -*.mexa64 -*.mexmaci64 -*.mexw64 -*.mexw32 -*.p # protected code *.dll *.so *.dylib -*.exe *.o *.obj +*.pdb -# Simulink generated & build files -slprj/ -*_grt_rtw/ -*_ert_rtw/ -*_slprj/ -*.slxc -*.slxp -build/ -CodeGenReport/ -rtwtypes.h -rtw_*.c -rtw_*.h +# Large data directories (keep out of repo) +sac_MSH/ +sac_Romania/ +sac_Toba/ -# Simulink coverage and test artifacts -coverage/ -coverage_*.xml -modelCoverageReport/ -slcovreport/ - -# Auto-generated files (Simulink Designer/Requirements) -requirements/ -reqs/ -TraceabilityReports/ -*.reqs +# Binaries, archives, generated distributions +bin/ +*.zip +*.tar +*.tar.gz +*.tgz -# Test results and logs -TestResults/ -tests_results/ -*.tap -*.trx -*.junit.xml +# Logs, test results, coverage *.log -*.out - -# Documentation build artifacts -docs/_build/ -doc/build/ -html/ -*.pdf -*.chm - -# MATLAB toolbox packaging / installation artifacts -*.mltbx -*.mlappinstall +TestResults/ +coverage/ -# Editor / IDE +# IDE / editor .vscode/ -.idea/ *.sublime-workspace *.sublime-project +*.swp -# OS files -.DS_Store -Thumbs.db -desktop.ini +# Git metadata / lock files +.gitattributes.lock -# Python interop (if used) +# Python cache (if any) __pycache__/ *.pyc -*.pyo -env/ -venv/ -.venv/ - -# Node / web tooling (if used in docs) -node_modules/ -dist/ -build/ - -# Containers / CI -*.tgz -*.tar.gz - -# MATLAB Online specific -.matlab_history -.matlab_crash_dump_* - -# Optional: user-specific MATLAB files -history.m -preferences.mat - -# Optional: large data files (enable if you don't want data in repo) -# data/ -# datasets/ -# *.mat -# Keep Git from trying to track generated files from builds -# (uncomment and adapt if you build artifacts locally) -# /bin/ -# /lib/ -# /build/ diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index e08d3a0..0000000 --- a/.gitmodules +++ /dev/null @@ -1,7 +0,0 @@ - -[submodule "IRtools"] - path = IRtools - url = https://github.com/jnagy1/IRtools -[submodule "IRTools"] - path = IRTools - url = https://github.com/jnagy1/IRtools.git diff --git a/IRtools.zip b/IRtools.zip deleted file mode 100644 index 30b984a..0000000 Binary files a/IRtools.zip and /dev/null differ diff --git a/MuRAT.m b/MuRAT.m new file mode 100644 index 0000000..c9ea56a --- /dev/null +++ b/MuRAT.m @@ -0,0 +1,159 @@ +%[text] # MuRAT Multi-Resolution (seismic) Attenuation Tomography +%[text] **SCOPE**: **A code for 3D attenuation-scattering-absorption tomography** +%[text] SYSTEM: The program works on all Mac, Windows and Linux computers where it has been tried. +%[text] MATLAB Version: Matlab 2025b +%[text] TOOLBOXES: +%[text] - Curve Fitting Toolbox +%[text] - Mapping Toolbox +%[text] - Optimization and Global Optimization Toolboxes +%[text] - Signal Processing Toolbox. +%[text] - Parallel Computing Toolbox is highly recommended for speed. \ +%[text] TEMPLATES: Three sample datasets (Mount St. Helens, Romania and Toba) and corresponding inputs are available in the root MuRAT folder. +%[text] INSTRUCTIONS: The current release (4.0) works following these steps: +%[text] 1. Download or branch the package at [https://github.com/LucaDeSiena/MuRAT](https://github.com/LucaDeSiena/MuRAT). +%[text] 2. Build your own input file (.m or .mlx) - each field is described in the attached Documentation.pdf and README.txt. +%[text] 3. Create your *sac\_data* folder. The codes in the *Utilities\_Matlab* folder, especially those in the *PrePostProcessing* subfolder, can be useful to check data and outputs, especially the necessary parameters are in the headers of the SAC files. +%[text] 4. Run this file and select the name of the input file you created beforhand. \ +%[text] Author: L. De Siena, January 2026 +%[text] ## INPUTS AND CHECKS +addpath(fullfile(pwd,'bin')); + +r = fullfile(pwd,'Utilities_Matlab'); + +% Split genpath into cell array of full folder paths +parts = strsplit(genpath(r), pathsep); +parts = parts(~cellfun(@isempty, parts)); + +% Filters: exclude .git, hidden folders (start with .), and 'private' +isBad = @(p) contains(p, [filesep '.git' filesep], 'IgnoreCase', true) ... + | contains(p, [filesep '.svn' filesep], 'IgnoreCase', true) ... + | contains(p, [filesep 'private' filesep], 'IgnoreCase', true) ... + | any( startsWith( split(p, filesep), '.' ) ); + +% Keep folders that pass filter and contain at least one .m file +keepMask = false(size(parts)); +for k = 1:numel(parts) + p = parts{k}; + if ~isBad(p) + % fast check for .m files (not recursive) + if ~isempty(dir(fullfile(p,'*.m'))) + keepMask(k) = true; + end + end +end + +addList = parts(keepMask); +if ~isempty(addList) + addpath(strjoin(addList, pathsep), '-end'); +end + +% Ask user for input file +[file, path]= uigetfile({'*.m;*.mlx','MuRAT input files (*.m, *.mlx)'; '*.*','All Files'}); +if isequal(file,0) + error('No input file selected.'); +end +inputFile = fullfile(path, file); +fprintf('Using input file %s\n', inputFile); + +% Run or load input file safely +[~,~,ext] = fileparts(file); +switch lower(ext) + case '.mlx' + run(inputFile); + case '.m' + run(inputFile); + otherwise + error('Unsupported input file extension: %s', ext); +end +tStart = tic; + +% --- Validation and ordering --- +Murat = Murat_checks(Murat); +Murat.input = orderfields(Murat.input); + +% --- Ensure temp folder exists --- +tempFolder = fullfile(pwd,'temp'); +if ~exist(tempFolder,'dir'), mkdir(tempFolder); end +save(fullfile(tempFolder,'Murat_checks.mat'), 'Murat','-v7'); + +tCheck = toc(tStart); +fprintf('Time spent on checks: %.2f s\n', tCheck); +%% +%[text] ## SEISMIC DATA PROCESSING AND FORWARD MODELLING +load(fullfile(tempFolder,'Murat_checks.mat'),'Murat'); + +% Ask user +useParallel = Murat.input.workers; + +% Try to prepare parallel pool only if requested +if ~isempty(useParallel) + % Check for Parallel Computing Toolbox + hasPCT = license('test','Distrib_Computing_Toolbox') || license('test','Parallel_Computing_Toolbox'); + if ~hasPCT + warning('Parallel Computing Toolbox not available. Falling back to sequential.'); + useParallel = false; + else + % Start pool if none exists + pool = gcp('nocreate'); + if isempty(pool) + try + pool= useParallel; % optionally specify NumWorkers + catch ME + warning('Failed to start parpool: %s. Falling back to sequential.'); + useParallel = false; + end + end + end +end + +tDataStart = tic; + +if useParallel + Murat = Murat_dataParallelized(Murat); +else + Murat = Murat_data(Murat); +end +save(fullfile(tempFolder,'Murat_forward.mat'), 'Murat','-v7'); +tData = toc(tDataStart); +fprintf('Time spent on data processing: %.2f s\n', tData); +%% +%[text] ## TOMOGRAPHIC INVERSIONS +tInvStart = tic; +load(fullfile(tempFolder,'Murat_forward.mat'),'Murat'); +Murat = Murat_inversion(Murat); +% Save with -v7.3 if large +info = whos('Murat'); +if info.bytes > 2e9 + save(fullfile(tempFolder,'Murat_inverse.mat'),'Murat','-v7.3'); +else + save(fullfile(tempFolder,'Murat_inverse.mat'),'Murat','-v7'); +end +tInv = toc(tInvStart); +fprintf('Inversion completed (%.2f s)\n', tInv); +%% +%[text] ## PLOTTING and CLEANING UP +tPlotStart = tic; +Murat = Murat_plot(Murat); + +outFolder = Murat.input.label; + +info = whos('Murat'); % re-check size +if info.bytes > 2e9 + save(fullfile(outFolder,'Murat.mat'),'Murat','-v7.3'); +else + save(fullfile(outFolder,'Murat.mat'),'Murat','-v7'); +end + +tPlot = toc(tPlotStart); +fprintf('Plotting completed (%.2f s)\n', tPlot); + +totalTime = toc(tStart); +fprintf('Total run time: %.2f s\n', totalTime); + +clearvars -except Murat tCheck tData tInv tPlot totalTime + +%[appendix]{"version":"1.0"} +%--- +%[metadata:view] +% data: {"layout":"inline","rightPanelPercent":40} +%--- diff --git a/MuRAT3.mlx b/MuRAT3.mlx deleted file mode 100644 index b61c014..0000000 Binary files a/MuRAT3.mlx and /dev/null differ diff --git a/Murat_inputMSH.m b/Murat_inputMSH.m new file mode 100644 index 0000000..4c9635f --- /dev/null +++ b/Murat_inputMSH.m @@ -0,0 +1,162 @@ +%[text:tableOfContents]{"heading":"Table of Contents"} +%[text] %[text:anchor:T_C5277B6D] # INPUT MuRAT - MSH +%[text] This is an input file for the program Multi-Resolution Attenuation Tomography (MuRAT), version 3. It refers to the following area: +%[text] ``` +%[text] MOUNT ST HELENS VOLCANO +%[text] ``` +%[text] **The working directory is the folder where you downloaded MuRAT: move it anywhere in your system.** +%[text] %[text:anchor:H_E5868F9D] ## GENERAL FIELDS +%[text] The user needs a data directory containing *.sac* files (beware of the difference between *.sac* and *.SAC*). If you want to use two or three-component recordings, you have to store them into a single folder, but the three components must be in the exact order **E,N,Z**. For an example see Toba. For this example we will use data in the **sac\_MSH** folder. Inside this folder, only vertical (*Z*) seismograms are stored: +Murat.input.dataDirectory = 'sac_MSH'; +%[text] ![](text:image:1652) +%[text] Best practice is to create a new folder to store your results. The code will create this folder inside the working directory. Specify the name of the folder that will store text files and figures, and will appear in your working directory: +Murat.input.label = 'MSH'; +%[text] ![](text:image:4e51) +%[text] In MuRAT3D you can choose between a sequential or parallelized forward loop. In the parallelized case, just set a number of workers (cores). In this example, we are working with the parallelized code and a computer with 8 cores. For a sequential run, set *Murat.input.workers* as empty (**\[\]**). +Murat.input.workers = []; +%% +%[text] %[text:anchor:H_5E594BA2] ## WAVEFORM DATA +%[text] Set all the variables required by data processing. This includes data choises, as setting the name of the variables in SAC and all the attributes that are needed for the three kinds of imaging. The routines for loading the files have been mostly created by [Zhigang Peng ](http://geophysics.eas.gatech.edu/people/zpeng/)and co-workers and downloaded from their [Introduction to SAC](http://geophysics.eas.gatech.edu/people/zpeng/Teaching/Sac_Tutorial_2006/) webpage. +%[text] MuRAT3D is designed to work with *sac* files only, so it is necessary to set the name of the variable containing their pickings and zero time. Files have to have populated headers. Set the name of the variable containing the origin time, P-wave pickings and S-wave pickings - in this example there is only the mandatory P-wave picking. If you don't have the origin time and S-wave times at hand you can mark them as empty (**\[\]**): +Murat.input.originTime = []; +Murat.input.PTime = 'a'; +Murat.input.STime = []; +%[text] **SAChdr** is a structure that contains all the fields you saved in the SAC haeder. In this example the P-wave picking has been stored inside variable *a.* You always find these variables in the *times* subfield: +%[text] ![](text:image:1136) +%[text] ![](text:image:0b92) +%[text] Then choose the coherent phase you are analyzing - P-(**2**) or S-(**3**). In our case it is P- as we have no S-wave picking: +Murat.input.POrS = 2; +%[text] You need to set the central frequencies (Hz) according to your spectrograms. General practice is to vary it across your spectra (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)) for absorption and scattering mapping or focus on a given frequency ([De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014JB011372)) for direct-wave attenuation imaging. Here, they cover the interval \[3-18\] Hz: +Murat.input.centralFrequency = [3 6 12 18]; + +% Envelope smoothing time (in seconds) used in Murat_envelope.m. +% If not specified, a default value of 1.0 s is used. +Murat.input.envelopeSmoothTime = 1; +%[text] You can work with 1 vertical or horizontal (*1*), 2 horizontal (*2*) or three components(*3*). If using more than one component, the order *MUST BE: WE, SN, Vertical or SN, WE,* Vertical. In this example we work with the vertical component only: +Murat.input.components = 1; +%[text] Finally, you can opt to decluster your data events. The code will divide the inversion grid by the following factor and select the best earthquake located in the block among all others. Set it to empty if you want to opt out (**\[\]**). +Murat.input.declustering = 5; +%% +%[text] %[text:anchor:H_CE0E35C8] ## PEAK DELAY +%[text] Scattering (peak delay) measurements rely on the existence of coherent waves. Peak delay is a standard measurements of forward scattering in regional scale-mapping since [Takahashi et al. 2007, GJI](https://academic.oup.com/gji/article/168/1/90/581022?login=true). Here, we approach the problem by adding a ray-tracing strategy to the system, assuming that the sensitivity follows the seismic ray. Also, we bring it to 3D and apply it to P-waves (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)). +%[text] To do so we need to set a minimum peak delay (*s*) considering scattering in the area and frequency. If using P wave this is important, as they can be sometimes more energetic than S-waves, biasing our measurements. We also need to set the maximum peack-delay (*s*) to avoid picking surface waves. +Murat.input.minimumPeakDelay = 0.1; +Murat.input.maximumPeakDelay = 7; +%[text] The user has the option to keep only those peak delays whose logarithmic trend is nearest to the average trend obtained by removing geometrical spreading. See [Napolitano et al. 2020 GF](https://www.sciencedirect.com/science/article/pii/S1674987119301999) and [Gabrielli et al. 2023, GRL](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL103132), for details. Set the following variables to the amount of standard deviations from the average trend you want to consider - the selection will be apparent in the Peak Delay test results. Set high numbers to consider all your data. +Murat.input.stDevPD = 2; +%% +%[text] %[text:anchor:H_0CC05CE8] ## DIRECT WAVE ATTENUATION +%[text] Total attenuation (inverse Q) measurements also relies on the existence of coherent waves. Total attenuation with the coda-normalization method is today a standard in volcano tomography ([Del Pezzo et al. 2006, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920106001282), [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2009JB006938); [De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JB011372), [Prudencio et al. 2015](https://link.springer.com/article/10.1007/s10712-015-9322-6), [Prudencio & Manga, 2020](https://academic.oup.com/gji/article-abstract/220/3/1677/5650518). +%[text] The method relies on the measurements of both direct and coda wave energy. Then we set the length of the window used to measure direct-wave energy, trying to smooth radiation pattern effects. The code uses the same length to measure noise energy. Discussions on the topic can be found in [De Siena et al. 2009, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920108003178?casa_token=FBQxspuFhPcAAAAA:P1a7DnDvjJpf9HVV1JzKsX-fzONnmmuUYMOZhAKOdRd_5Al7EHb5AydQAhjAwiIyBhMOq1xKNA) and [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009JB006938), but the choise is as usual dependent on data: +Murat.input.bodyWindow = 1; +%[text] We also need the start of the window used to measure noise a few seconds after the start of the recording (in seconds). +Murat.input.startNoise = 5; +%[text] The coda-to-noise energy ratio is used by the weighted inversion. Here you set the minimum accepted energy ratio. +Murat.input.tresholdNoise = 3; +%% +%[text] %[text:anchor:H_6BEFB091] ## CODA ATTENUATION +%[text] Coda attenuation (inverse Qc) is measured from the decay of the coda with lapse time from the origin time of the earthquake, and requires energetic scattering. Qc is a well know parameter for assesing tectonic structures ([Sato et al. 2012, Springer](https://link.springer.com/book/10.1007/978-3-642-23029-5)). In recent years it has been used as an imaging attribute at the regional ([Calvet et al. 2013, Tectonophysics](https://www.sciencedirect.com/science/article/abs/pii/S0040195113005490); [Borleanu et al. 2017, Tectonophysics](https://www.sciencedirect.com/science/article/pii/S0040195117301476)), fault ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999); [Sketsiou et al. 2020, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920119302122)) and, especially, volcanic scales ([Prudencio et al. 2013a, GJI](https://academic.oup.com/gji/article/195/3/1957/2874185?login=true); [Prudencio et al. 2013b GJI](https://academic.oup.com/gji/article/195/3/1942/626933); [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437); [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507); [Gabrielli et al. 2020, GJI](https://academic.oup.com/gji/article-abstract/222/1/169/5814316)). +%[text] %[text:anchor:H_457B51A4] ### Lapse times and sensitivity kernels +%[text] In MuRAT3D, we use the full 3D computational kernels devised by [Del Pezzo et al. 2018, Geosciences](https://www.mdpi.com/2076-3263/8/5/175) in the inversion approach proposed first by [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507). +%[text] Here you choose the start of the window used to measure coda wave energy and model kernels. The starting time of the coda window can be set directly in seconds ('**Constant**'), from the envelope peak ('**Peak**'), or depending on the P- or S-wave travel time, for example twice the S-wave travel time ('**Travel**'). If the chosen method is **Constant**, set start and length of the window. +Murat.input.lapseTimeMethod = 'Constant'; +%[text] If the chosen method is **Constant**, set the start of the window in seconds after the origin time. If it is **Peak**, set it to **\[\]**. If it is **Travel** select *Murat.input.startLapseTime* as the moltiplicative factor of the phase you are using (e.g., **2** or **3**). Avoid this method if you do not know the S-wave time. +Murat.input.startLapseTime = 15; +%[text] Finally set the length of the coda window in seconds. The true lapse time at which we calculate the kernels is half of the window. The window is also used (after normalizing for its length) in the coda normalization method. +Murat.input.codaWindow = 15; +%[text] Set maximum travel time if you want to exclude traces beyond a certain value, else put \[Inf\]. +Murat.input.mintravel = 0.2; +Murat.input.maxtravel = 13; +%[text] The **MLTWA** is the standard method to find the average parameters necessary to calculate the kernels. It provides albedo and inverse extinction length: +Murat.input.albedo = [0.8 0.6 0.4 0.2]; +Murat.input.iExtinctionLength = [0.12 0.12 0.1 0.1]; % D = vS./Le_1/3./(1-B0) +%[text] As the kernels are computational intensive and require a full matrix of nodes to avoid singularities, we also use a computational factor to reduce the computational time. This number divides the input grid, meaning that higher numbers give more precise results at the expene of computational time. Minimum is **1**. A figure will output the kernel in this grid. +Murat.input.kernelTreshold = 2; +%[text] %[text:anchor:H_38D18D3D] ### Measurement of Qc +%[text] MuRAT3D implements either a linearised approach or a grid search approach to measure Qc. The linearised approach is the standard proposed first by Aki (e.g., [Havskov et al. 2016, BSSA](https://www.researchgate.net/publication/303510878_Coda_Q_in_Different_Tectonic_Areas_Influence_of_Processing_Parameters)) to best fit Qc after taking the logarithm of the energy. The uncertainties are derived from the simple minimum R-squared (fitTresholdLinear) and needs to be defined by a number between 0 and 1. It is advisable to set a minimum of 0.1. +%[text] The non linear approach models energy data measured on one-second windows across the envelope and minimizes the difference between data and model with a 1D grid search algorithm ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999)). Uncertainties are given by the experimental probability density function of the misfit. In both cases, uncertainties play as a weight in the final inversion. In the second case, leave the fitTresholdLinear = **\[\]**. +%[text] The user needs to choose between the two options **'Linearized'** and **'NonLinear':** +Murat.input.QcMeasurement = 'NonLinear'; +Murat.input.fitTresholdLinear = 0.006; +%% +%[text] %[text:anchor:H_33DD6EF5] ## GEOMETRY AND VELOCITY +%[text] This section sets the details of the inversion grid and availability of velocity model. In MuRAT3D the coordinates of the model are in lat/lon, then they get converted in km. The vertical is in altitude above sea level. The velocity model can be 1D or 3D - if 3D all points must be given in lat/long formats. You start by setting the origin and end points of your inversion grid. +Murat.input.origin = [45.9805 -122.5030 3350]; +Murat.input.end = [46.3900 -122.0000 -22650]; +%[text] Then you need to set the number of nodes in the three directions. This is obviously dependent on the scale of your area. You will be playing a lot with this to test your effective resolution capabilities. +Murat.input.gridLat = 10; +Murat.input.gridLong = 12; +Murat.input.gridZ = 6; +%[text] You will see all of your figures on three sections cutting the models WE (degrees), SN (degrees), and horizontally (meters or km) at: +Murat.input.sections = [46.12 -122.20 -1000]; +%[text] With this version of the code you are always using an underlying velocity model: the 3D is either unavailable (***0***) or a available (***1***) velocity model. For the 1D case MuRAT provides both the *iasp91.txt*, the standard [IASPEI velocity model](https://academic.oup.com/gji/article/105/2/429/705789) and the more accurate [Lithos model](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JB010626), and expands any of them to a false 3D. However, a standard crustal model is generally available everywhere on the Earth, so use that, but change it to the same format as the file provided: first column is depth, second is distance from the centre of the Earth, then third and fourth are P- and S-wave velocity. Store the file in the folder **velocity\_models**. See the Romania example file. +%[text] At Mount St. Helens, we use the 3D local earthquake tomography model of [Waite and Moran, 2009,JVGR](https://www.sciencedirect.com/science/article/pii/S0377027309000742), (*modvMSH.txt*), stored in the folder **velocity\_model:** +%[text] ![Screenshot 2021-10-24 at 15.30.15.png](text:image:620c) +%[text] whose coordinates are Lat/Long/Altitude/Velocity (P or S depending on the phase you are using) in meters. So we set: +Murat.input.availableVelocity = 1; +Murat.input.namev = 'modvMSH.txt'; +%[text] Even if we set the velocity model we still need the average crustal velocities if you have no info of origin time. It is highly recommended you have the origin in the haeder, at variables **'o'**! +Murat.input.averageVelocityP = 6; +Murat.input.averageVelocityS = 2.5; +%% +%[text] %[text:anchor:H_68B4738F] ## INVERSION +%[text] The code implements: +%[text] - a standard Tikhonov inversion based on singular value decomposition (**'Tikhonov'**), where we on the [regtools Matlab suite](https://de.mathworks.com/matlabcentral/fileexchange/52-regtools) from Per Christian Hansen. +%[text] - the particle swarm (**Particle**), simulated annealing (**Annealing**), or genetic algorithm (**Genetic**) solvers from the optimization toolbox. \ +%[text] In the last case, the user can choose the maximum number of iterations after which the optimization will stop. The user can also choose the maximum number of iterations where the misfit stalls after which the optimization will stop. Otherwise leave empty. +Murat.input.inversionMethod = 'Tikhonov'; +Murat.input.MaximumIterations = 2e3; +Murat.input.MaximumStallIterations = 100; +%[text] Set this to ***1*** to plot during computation: +%[text] - the L curves between residual and norm length ([Aster et al. 2013](https://www.sciencedirect.com/book/9780123850485/parameter-estimation-and-inverse-problems) - case **'Tikhonov'**). +%[text] - the decrease in misfit for the remaining cases. \ +Murat.input.PlotInversion = 0; +%[text] The user can set damping and smoothing parameters for Qc. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average inverse coda quality factor if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQc = [0.001 0.001 0.001 0.001]; +Murat.input.smoothingValueQc = [0 0 0 0]; +%[text] The user can set damping and smoothing parameters for Q. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average energy ratio if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQ = [0.01 0.01 0.01 0.01]; +Murat.input.smoothingValueQ = [0 0 0 0]; +%[text] ### Synthetic Testing +%[text] A great reference for the best sort of testing is [Rawlinson & Spakman, 2016](https://academic.oup.com/gji/article/205/2/1221/692880?login=true). If you want to test you results you need to create a checkerboard. The size of the checks can be twice (*2*) or four times (*4*) the node spacing. Values are minimum and maximum of final measured parameters. +Murat.input.sizeCheck = 2; +%[text] In MuRAT3D you can also set the origin and the end locations of a spike as well as its attenuation value: +Murat.input.spikeLocationOrigin = [46.05 -122.40 0]; +Murat.input.spikeLocationEnd = [46.12 -122.30 -6000]; +Murat.input.spikeValue = 0.01; +%% +%[text] ## PLOTTING +%[text] Set to **1** if you want to plot the figures relative to: +%[text] *Rays* +Murat.input.PlotRays = 1; +%[text] *Tests* +Murat.input.PlotTests = 1; +%[text] *Results* +Murat.input.PlotResults = 1; +%[text] *Checkerboards* +Murat.input.PlotCheckers = 1; +%[text] *Spikes* +Murat.input.PlotSpikes = 1; +%[text] *Parameters* +Murat.input.PlotParameters = 1; + +%[appendix]{"version":"1.0"} +%--- +%[metadata:view] +% data: {"layout":"inline","rightPanelPercent":40} +%--- +%[text:image:1652] +% data: 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","width":510} 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","width":506} 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+%--- diff --git a/Murat_inputMSH.mlx b/Murat_inputMSH.mlx deleted file mode 100644 index 966c68e..0000000 Binary files a/Murat_inputMSH.mlx and /dev/null differ diff --git a/Murat_inputRomania.m b/Murat_inputRomania.m new file mode 100644 index 0000000..870f842 --- /dev/null +++ b/Murat_inputRomania.m @@ -0,0 +1,161 @@ +%[text:tableOfContents]{"heading":"Table of Contents"} +%[text] %[text:anchor:T_C5277B6D] # INPUT MuRAT - Romania +%[text] This is an input file for the program Multi-Resolution Attenuation Tomography (MuRAT), version 4. It refers to the following area: +%[text] ``` +%[text] ROMANIA +%[text] ``` +%[text] **The working directory is the folder where you downloaded MuRAT: move it anywhere in your system.** +%[text] %[text:anchor:H_E5868F9D] ## GENERAL FIELDS AND FIGURES +%[text] The user needs a data directory containing *.sac* files (beware of the difference between *.sac* and *.SAC*). If you want to use two or three-component recordings, you have to store them into a single folder, but the three components must be in the exact order **E,N,Z**. For an example see Toba. For this example we will use data in the **sac\_Romania** folder. Inside this folder, only horizontal (*North*) seismograms are stored: +Murat.input.dataDirectory = 'sac_Romania'; +%[text] ![](text:image:55db) +%[text] Best practice is to create a new folder to store your results. The code will create this folder inside the working directory. Specify the name of the folder that will store text files and figures, and will appear in your working directory: +Murat.input.label = 'Romania'; +%[text] ![](text:image:16e3) +%[text] In MuRAT3D you can choose between a sequential or parallelized forward loop. In the parallelized case, just set a number of workers (cores). For a sequential run, set *Murat.input.workers* as empty (**\[\]**). +Murat.input.workers = []; +%% +%[text] %[text:anchor:H_D8CB3700] ## WAVEFORM DATA +%[text] Set all the variables required by data processing. This includes data choises, as setting the name of the variables in SAC and all the attributes that are needed for the three kinds of imaging. The routines for loading the files have been mostly created by [Zhigang Peng ](http://geophysics.eas.gatech.edu/people/zpeng/)and co-workers and downloaded from their [Introduction to SAC](http://geophysics.eas.gatech.edu/people/zpeng/Teaching/Sac_Tutorial_2006/) webpage. +%[text] MuRAT3D is designed to work with *sac* files only, so it is necessary to set the name of the variable containing their pickings and zero time. Files have to have populated headers. Set the name of the variable containing the origin time, P-wave pickings and S-wave pickings - in this example there are the mandatory P-wave picking and optional origin time. If you don't have the S-wave times at hand you can mark them as empty (**\[\]**): +Murat.input.originTime = 'o'; +Murat.input.PTime = 't0'; +Murat.input.STime = 't0'; +%[text] **SAChdr** is a structure that contains all the fields you saved in the SAC haeder. In this example the P-wave picking has been stored inside variable *a.* You always find these variables in the *times* subfield: +%[text] ![](text:image:5d6d) +%[text] ![](text:image:5657) +%[text] Then choose the coherent phase you are analyzing - P-(**2**) or S-(**3**). In our case it is P- as we have no S-wave picking. +Murat.input.POrS = 3; +%[text] You need to set the central frequencies (Hz) according to your spectrograms. General practice is to vary it across your spectra (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)) for absorption and scattering mapping or focus on a given frequency ([De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014JB011372)) for direct-wave attenuation imaging. Here, they cover the interval \[3-18\] Hz. +Murat.input.centralFrequency = [3 6 12 18]; + +% Envelope smoothing time (in seconds) used in Murat_envelope.m. +% If not specified, a default value of 1.0 s is used. +Murat.input.envelopeSmoothTime = 1; +%[text] You can work with 1 vertical or horizontal (*1*), 2 horizontal (*2*) or three components(*3*). If using more than one component, the order *MUST BE: WE, SN, Vertical or SN, WE, Vertica*l. In this example we work with the horizontal component only. +Murat.input.components = 1; +%[text] Finally, you can opt to decluster your data events. The code will divide the inversion grid by the following factor and select the best earthquake located in the block among all others. Set it to empty if you want to opt out (**\[\]**). +Murat.input.declustering = 5; +%% +%[text] %[text:anchor:H_CE0E35C8] ## PEAK DELAY +%[text] Scattering (peak delay) measurements rely on the existence of coherent waves. Peak delay is a standard measurements of forward scattering in regional scale-mapping since [Takahashi et al. 2007, GJI](https://academic.oup.com/gji/article/168/1/90/581022?login=true). Here, we approach the problem by adding a ray-tracing strategy to the system, assuming that the sensitivity follows the seismic ray. Also, we bring it to 3D and apply it to P-waves (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)). +%[text] To do so we need to set a minimum peak delay (*s*) considering scattering in the area and frequency. If using P wave this is crucial, as they can be sometimes more energetic than S-waves, biasing our measurements. We also need to set the maximum peack-delay (*s*) to avoid picking surface waves. +Murat.input.minimumPeakDelay = 0.1; +Murat.input.maximumPeakDelay = 10; +%[text] The user has the option to keep only those peak delays whose logarithmic trend is nearest to the average trend obtained by removing geometrical spreading. See [Napolitano et al. 2020 GF](https://www.sciencedirect.com/science/article/pii/S1674987119301999) and [Gabrielli et al. 2023, GRL](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL103132), for details. Set the following variables to the amount of standard deviations from the average trend you want to consider - the selection will be apparent in the Peak Delay test results. Set high numbers to consider all your data. +Murat.input.stDevPD = 2; +%% +%[text] %[text:anchor:H_0CC05CE8] ## DIRECT WAVE ATTENUATION +%[text] Total attenuation (inverse Q) measurements also relies on the existence of coherent waves. Total attenuation with the coda-normalization method is today a standard in volcano tomography ([Del Pezzo et al. 2006, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920106001282), [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2009JB006938); [De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JB011372), [Prudencio et al. 2015](https://link.springer.com/article/10.1007/s10712-015-9322-6), [Prudencio & Manga, 2020](https://academic.oup.com/gji/article-abstract/220/3/1677/5650518). +%[text] Then we set the length of the window used to measure direct-wave energy, trying to smooth radiation pattern effects. The code uses the same length to measure noise energy. Discussions on the topic can be found in [De Siena et al. 2009, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920108003178?casa_token=FBQxspuFhPcAAAAA:P1a7DnDvjJpf9HVV1JzKsX-fzONnmmuUYMOZhAKOdRd_5Al7EHb5AydQAhjAwiIyBhMOq1xKNA) and [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009JB006938), but the choise is as usual dependent on data: +Murat.input.bodyWindow = 1; +%[text] We also need the start of the window used to measure noise a few seconds after the start of the recording (in seconds). +Murat.input.startNoise = 5; +%[text] The coda-to-noise energy ratio is used by the weighted inversion. Here you set the minimum accepted energy ratio. +Murat.input.tresholdNoise = 3; +%% +%[text] %[text:anchor:H_6409BBD8] ## CODA ATTENUATION +%[text] Coda attenuation (inverse Qc) is measured from the decay of the coda with lapse time from the origin time of the earthquake, and requires energetic scattering. Qc is a well know parameter for assesing tectonic structures ([Sato et al. 2012, Springer](https://link.springer.com/book/10.1007/978-3-642-23029-5)). In recent years it has been used as an imaging attribute at the regional ([Calvet et al. 2013, Tectonophysics](https://www.sciencedirect.com/science/article/abs/pii/S0040195113005490); [Borleanu et al. 2017, Tectonophysics](https://www.sciencedirect.com/science/article/pii/S0040195117301476)), fault ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999); [Sketsiou et al. 2020, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920119302122)) and, especially, volcanic scales ([Prudencio et al. 2013a, GJI](https://academic.oup.com/gji/article/195/3/1957/2874185?login=true); [Prudencio et al. 2013b GJI](https://academic.oup.com/gji/article/195/3/1942/626933); [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437); [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507); [Gabrielli et al. 2020, GJI](https://academic.oup.com/gji/article-abstract/222/1/169/5814316)). +%[text] %[text:anchor:H_D450FCC5] ### Lapse times and sensitivity kernels +%[text] In MuRAT3D, we use the full 3D computational kernels devised by [Del Pezzo et al. 2018, Geosciences](https://www.mdpi.com/2076-3263/8/5/175) in the inversion approach proposed first by [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507). +%[text] Here you choose the start of the window used to measure coda wave energy and model kernels. The starting time of the coda window can be set directly in seconds ('**Constant**'), from the envelope peak ('**Peak**'), or depending on the P- or S-wave travel time, for example twice the S-wave travel time ('**Travel**'). If the chosen method is **Constant**, set start and length of the window. +Murat.input.lapseTimeMethod = 'Constant'; +%[text] If the chosen method is **Constant**, set the start of the window in seconds after the origin time. If it is **Peak**, set it to **\[\]**. If it is **Travel** select *Murat.input.startLapseTime* as the moltiplicative factor of the phase you are using (e.g., **2** or **3**). Avoid this method if you do not know the S-wave time. +Murat.input.startLapseTime = 30; +%[text] Finally set the length of the coda window in seconds. The true lapse time at which we calculate the kernels is half of the window. The window is also used (after normalizing for its length) in the coda normalization method. +Murat.input.codaWindow = 30; +%[text] Set maximum travel time if you want to exclude traces beyond a certain value, else put \[Inf\]. +Murat.input.mintravel = 0.2; +Murat.input.maxtravel = Inf; +%[text] The **MLTWA** is the standard method to find the average parameters necessary to calculate the kernels. It provides albedo and extinction length: +Murat.input.albedo = [0.5 0.5 0.5 0.5]; +Murat.input.iExtinctionLength = [0.02 0.02 0.02 0.02]; % D = vS./Le_1/3./(1-B0) +%[text] As the kernels are computational intensive and require a full matrix of nodes to avoid singularities, we also use a computational factor to reduce the computational time. This number divides the input grid, meaning that higher numbers give more precise results at the expene of computational time. Minimum is **1**. A figure will output the kernel in this grid. +Murat.input.kernelTreshold = 2; +%[text] %[text:anchor:H_865BAF76] ### Measurement of Qc +%[text] MuRAT3D implements either a linearised approach or a grid search approach to measure Qc. The linearised approach is the standard proposed first by Aki (e.g., [Havskov et al. 2016, BSSA](https://www.researchgate.net/publication/303510878_Coda_Q_in_Different_Tectonic_Areas_Influence_of_Processing_Parameters)) to best fit Qc after taking the logarithm of the energy. The uncertainties are derived from the simple minimum R-squared (fitTresholdLinear) and needs to be defined by a number between 0 and 1. It is advisable to set a minimum of 0.1. +%[text] The non linear approach models energy data measured on one-second windows across the envelope and minimizes the difference between data and model with a 1D grid search algorithm ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999)). Uncertainties are given by the experimental probability density function of the misfit. In both cases, uncertainties play as a weight in the final inversion. In the second case, leave the fitTresholdLinear = **\[\]**. +%[text] The user needs to choose between the two options **'Linearized'** and **'NonLinear':** +Murat.input.QcMeasurement = 'Linearized'; +Murat.input.fitTresholdLinear = 0.3; +%% +%[text] %[text:anchor:H_33DD6EF5] ## GEOMETRY AND VELOCITY +%[text] This section sets the details of the inversion grid and availability of velocity model. In MuRAT3D the coordinates of the model are in lat/lon, then they get converted in km. The vertical is in altitude above sea level. The velocity model can be 1D or 3D - if 3D all poins must be given in lat/long formats. You start by setting the origin and end points of your inversion grid. +Murat.input.origin = [43 20 1000]; +Murat.input.end = [49 29 -50000]; +%[text] Then you need to set the number of nodes in the three directions. This is obviously dependent on the scale of your area. You will be playing a lot with this to test your effective resolution capabilities. +Murat.input.gridLat = 5; +Murat.input.gridLong = 9; +Murat.input.gridZ = 6; +%[text] You will see all of your figures on three sections cutting the models WE (degrees), SN (degrees), and horizontally (meters or km) at: +Murat.input.sections = [45.5 26.7 -7000]; +%[text] With this version of the code you are always using an underlying velocity model: the 3D is either unavailable (***0***) or a vailable (***1***) velocity model. For the 1D case MuRAT provides you *iasp91.txt*, the standard [IASPEI velocity model](https://academic.oup.com/gji/article/105/2/429/705789) and the more accurate [Lithos model](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JB010626), and expands any of them to a false 3D. However, a standard crustal model is generally available everywhere on the Earth, so use that, but change it to the same format as the file provided: first column is depth, second is distance from the centre of the Earth, then third and fourth are P- and S-wave velocity. Store the file in the folder **velocity\_models**. In Romania, we use *iasp91*: +%[text] ![Screenshot 2021-10-24 at 15.36.05.png](text:image:8f86) +%[text] %[text:anchor:H_43D6D438] So we set: +Murat.input.availableVelocity = 0; +Murat.input.namev = 'LITHO1.0.nc'; +%[text] Even if we set the velocity model we still need the average crustal velocities if you have no info of origin time. It is highly recommended you have the origin in the haeder, at variables **'o'**! +Murat.input.averageVelocityP = 6; +Murat.input.averageVelocityS = 3; +%% +%[text] %[text:anchor:H_68B4738F] ## INVERSION +%[text] The code implements: +%[text] - a standard Tikhonov inversion based on singular value decomposition (**'Tikhonov'**), where we on the [regtools Matlab suite](https://de.mathworks.com/matlabcentral/fileexchange/52-regtools) from Per Christian Hansen. +%[text] - the particle swarm (**Particle**), simulated annealing (**Annealing**), or genetic algorithm (**Genetic**) solvers from the optimization toolbox. \ +%[text] In the last case, the user can choose the maximum number of iterations after which the optimization will stop. The user can also choose the maximum number of iterations where the misfit stalls after which the optimization will stop. Otherwise leave empty. +Murat.input.inversionMethod = 'Tikhonov'; +Murat.input.MaximumIterations = 2e3; +Murat.input.MaximumStallIterations = 100; +%[text] Set this to ***1*** to plot: +%[text] - the L curves between residual and norm length ([Aster et al. 2013](https://www.sciencedirect.com/book/9780123850485/parameter-estimation-and-inverse-problems) - case **'Tikhonov'**). +%[text] - the decrease in misfit for the remaining cases. \ +Murat.input.PlotInversion = 0; +%[text] The user can set damping and smoothing parameters for Qc. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average inverse coda quality factor if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQc = [1.0e-06 1.0e-06 3.0e-06 3.0e-06]; +Murat.input.smoothingValueQc = []; +%[text] The user can set damping and smoothing parameters for Q. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average energy ratio if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQ = [0.01 0.01 0.01 0.01]; +Murat.input.smoothingValueQ = []; +%[text] ### Synthetic Testing +%[text] A great reference for the best sort of testing is [Rawlinson & Spakman, 2016](https://academic.oup.com/gji/article/205/2/1221/692880?login=true). If you want to test you results you need to create a checkerboard. The size of the checks can be twice (*2*) or four times (*4*) the node spacing. Values are minimum and maximum of final measured parameters. +Murat.input.sizeCheck = 2; +%[text] In MuRAT3D you can also set the origin and the end locations of a spike as well as its attenuation value: +Murat.input.spikeLocationOrigin = [44 23 -10000]; +Murat.input.spikeLocationEnd = [46 25 -30000]; +Murat.input.spikeValue = 0.01; +%% +%[text] ## PLOTTING +%[text] Set to **1** if you want to plot the figures relative to: +%[text] *Rays* +Murat.input.PlotRays = 1; +%[text] *Tests* +Murat.input.PlotTests = 1; +%[text] *Results* +Murat.input.PlotResults = 1; +%[text] *Checkerboards* +Murat.input.PlotCheckers = 1; +%[text] *Spikes* +Murat.input.PlotSpikes = 1; +%[text] *Parameters* +Murat.input.PlotParameters = 1; + +%[appendix]{"version":"1.0"} +%--- +%[metadata:view] +% data: {"layout":"inline","rightPanelPercent":40} +%--- +%[text:image:55db] +% data: 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\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","width":510} 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","width":506} 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+%--- diff --git a/Murat_inputRomania.mlx b/Murat_inputRomania.mlx deleted file mode 100644 index 21452e8..0000000 Binary files a/Murat_inputRomania.mlx and /dev/null differ diff --git a/Murat_inputToba.m b/Murat_inputToba.m new file mode 100644 index 0000000..87e501b --- /dev/null +++ b/Murat_inputToba.m @@ -0,0 +1,161 @@ +%[text:tableOfContents]{"heading":"Table of Contents"} +%[text] %[text:anchor:T_C5277B6D] # INPUT MuRAT - Toba +%[text] This is an input file for the program Multi-Resolution Attenuation Tomography (MuRAT), version 3. It refers to the following area: +%[text] ``` +%[text] TOBA CALDERA +%[text] ``` +%[text] **The working directory is the folder where you downloaded MuRAT: move it anywhere in your system.** +%[text] %[text:anchor:H_E5868F9D] ## GENERAL FIELDS +%[text] The user needs a data directory containing *.sac* files (beware of the difference between *.sac* and *.SAC*). If you want to use two or three-component recordings, you have to store them into a single folder, but the three components must be in the exact order **E,N,Z**. For an example see Toba. For this example we will use data in the **sac\_Toba** folder. Inside this folder, three-component (*W,N,Z*) seismograms are stored: +Murat.input.dataDirectory = 'sac_Toba'; +%[text] ![Screenshot 2021-10-24 at 16.21.37.png](text:image:3db7) +%[text] Best practice is to create a new folder to store your results. The code will create this folder inside the working directory. Specify the name of the folder that will store text files and figures, and will appear in your working directory: +Murat.input.label = 'Toba'; +%[text] ![](text:image:9b16) +%[text] In MuRAT3D you can choose between a sequential or parallelized forward loop. In the parallelized case, just set a number of workers (cores). In this example, we are working with the parallelized code and a computer with 8 cores. For a sequential run, set Murat.input.workers as empty (**\[\]**): +Murat.input.workers = []; +%% +%[text] %[text:anchor:H_9FC8E978] ## WAVEFORM DATA +%[text] Set all the variables required by data processing. This includes data choises, as setting the name of the variables in SAC and all the attributes that are needed for the three kinds of imaging. The routines for loading the files have been mostly created by [Zhigang Peng ](http://geophysics.eas.gatech.edu/people/zpeng/)and co-workers and downloaded from their [Introduction to SAC](http://geophysics.eas.gatech.edu/people/zpeng/Teaching/Sac_Tutorial_2006/) webpage. +%[text] MuRAT3D is designed to work with *sac* files only, so it is necessary to set the name of the variable containing their pickings and zero time. Files have to have populated headers. Set the name of the variable containing the origin time, P-wave pickings and S-wave pickings - in this example there is only the mandatory P-wave picking. If you don't have the origin time and S-wave times at hand you can mark them as empty (**\[\]**): +Murat.input.originTime = 'o'; +Murat.input.PTime = 'a'; +Murat.input.STime = 't0'; +%[text] **SAChdr** is a structure that contains all the fields you saved in the SAC haeder. In this example the P-wave picking has been stored inside variable *a.* You always find these variables in the *times* subfield: +%[text] ![](text:image:2abb) +%[text] ![](text:image:5755) +%[text] Then choose the coherent phase you are analyzing - P-(**2**) or S-(**3**). In our case it is S-wave picking. +Murat.input.POrS = 3; +%[text] You need to set the central frequencies (Hz) according to your spectrograms. General practice is to vary it across your spectra (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)) for absorption and scattering mapping or focus on a given frequency ([De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014JB011372)) for direct-wave attenuation imaging. Here, they cover the interval \[1.5-3\] Hz. +Murat.input.centralFrequency = [1.5 3]; + +% Envelope smoothing time (in seconds) used in Murat_envelope.m. +% If not specified, a default value of 1.0 s is used. +Murat.input.envelopeSmoothTime = 1; +%[text] You can work with 1 vertical or horizontal (*1*), 2 horizontal (*2*) or three components(*3*). If using more than one component, the order *MUST BE: WE, SN, Vertical or SN, WE,* Vertical. Here we use three components: +Murat.input.components = 3; +%[text] Finally, you can opt to decluster your data events. The code will divide the inversion grid by the following factor and select the best earthquake located in the block among all others. Set it to empty if you want to opt out (**\[\]**). +Murat.input.declustering = 5; +%% +%[text] %[text:anchor:H_CE0E35C8] ## PEAK DELAY +%[text] Scattering (peak delay) measurements rely on the existence of coherent waves. Peak delay is a standard measurements of forward scattering in regional scale-mapping since [Takahashi et al. 2007, GJI](https://academic.oup.com/gji/article/168/1/90/581022?login=true). Here, we approach the problem by adding a ray-tracing strategy to the system, assuming that the sensitivity follows the seismic ray. Also, we bring it to 3D and apply it to P-waves (see [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437)). +%[text] To do so we need to set a minimum peak delay (*s*) considering scattering in the area and frequency. If using P wave this is crucial, as they can be sometimes more energetic than S-waves, biasing our measurements. We also need to set the maximum peack-delay (*s*) to avoid picking surface waves. +Murat.input.minimumPeakDelay = 0.1; +Murat.input.maximumPeakDelay = 10; +%[text] The user has the option to keep only those peak delays whose logarithmic trend is nearest to the average trend obtained by removing geometrical spreading. See [Napolitano et al. 2020 GF](https://www.sciencedirect.com/science/article/pii/S1674987119301999) and [Gabrielli et al. 2023, GRL](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL103132)**,** See [Napolitano et al. 2020 GF](https://www.sciencedirect.com/science/article/pii/S1674987119301999) and [Gabrielli et al. 2023, GRL](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023GL103132), for details. Set the following variables to the amount of standard deviations from the average trend you want to consider - the selection will be apparent in the Peak Delay test results. Set high numbers to consider all your data. +Murat.input.stDevPD = 2; +%% +%[text] %[text:anchor:H_0CC05CE8] ## DIRECT WAVE ATTENUATION +%[text] Total attenuation (inverse Q) measurements also relies on the existence of coherent waves. Total attenuation with the coda-normalization method is today a standard in volcano tomography ([Del Pezzo et al. 2006, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920106001282), [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2009JB006938); [De Siena et al. 2014, JGR](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JB011372), [Prudencio et al. 2015](https://link.springer.com/article/10.1007/s10712-015-9322-6), [Prudencio & Manga, 2020](https://academic.oup.com/gji/article-abstract/220/3/1677/5650518). +%[text] The method relies on the measurements of both direct and coda wave energy. Then we set the length of the window used to measure direct-wave energy, trying to smooth radiation pattern effects. The code uses the same length to measure noise energy. Discussions on the topic can be found in [De Siena et al. 2009, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920108003178?casa_token=FBQxspuFhPcAAAAA:P1a7DnDvjJpf9HVV1JzKsX-fzONnmmuUYMOZhAKOdRd_5Al7EHb5AydQAhjAwiIyBhMOq1xKNA) and [De Siena et al. 2010, JGR](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009JB006938), but the choise is as usual dependent on data: +Murat.input.bodyWindow = 1; +%[text] We also need the start of the window used to measure noise a few seconds after the start of the recording (in seconds). +Murat.input.startNoise = 3; +%[text] The coda-to-noise energy ratio is used by the weighted inversion. Here you set the minimum accepted energy ratio. +Murat.input.tresholdNoise = 3; +%% +%[text] %[text:anchor:H_6409BBD8] ## CODA ATTENUATION +%[text] Coda attenuation (inverse Qc) is measured from the decay of the coda with lapse time from the origin time of the earthquake, and requires energetic scattering. Qc is a well know parameter for assesing tectonic structures ([Sato et al. 2012, Springer](https://link.springer.com/book/10.1007/978-3-642-23029-5)). In recent years it has been used as an imaging attribute at the regional ([Calvet et al. 2013, Tectonophysics](https://www.sciencedirect.com/science/article/abs/pii/S0040195113005490); [Borleanu et al. 2017, Tectonophysics](https://www.sciencedirect.com/science/article/pii/S0040195117301476)), fault ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999); [Sketsiou et al. 2020, PEPI](https://www.sciencedirect.com/science/article/pii/S0031920119302122)) and, especially, volcanic scales ([Prudencio et al. 2013a, GJI](https://academic.oup.com/gji/article/195/3/1957/2874185?login=true); [Prudencio et al. 2013b GJI](https://academic.oup.com/gji/article/195/3/1942/626933); [De Siena et al. 2016, EPSL](https://www.sciencedirect.com/science/article/abs/pii/S0012821X16300437); [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507); [Gabrielli et al. 2020, GJI](https://academic.oup.com/gji/article-abstract/222/1/169/5814316)). +%[text] %[text:anchor:H_D450FCC5] ### Lapse times and sensitivity kernels +%[text] In MuRAT3D, we use the full 3D computational kernels devised by [Del Pezzo et al. 2018, Geosciences](https://www.mdpi.com/2076-3263/8/5/175) in the inversion approach proposed first by [De Siena et al. 2017, GRL](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2017GL072507). +%[text] Here you choose the start of the window used to measure coda wave energy and model kernels. The starting time of the coda window can be set directly in seconds ('**Constant**'), from the envelope peak ('**Peak**'), or depending on the P- or S-wave travel time, for example twice the S-wave travel time ('**Travel**'). If the chosen method is **Constant**, set start and length of the window. +Murat.input.lapseTimeMethod = 'Constant'; +%[text] If the chosen method is **Constant**, set the start of the window in seconds after the origin time. If it is **Peak**, set it to **\[\]**. If it is **Travel** select *Murat.input.startLapseTime* as the moltiplicative factor of the phase you are using (e.g., **2** or **3**). Avoid this method if you do not know the S-wave time. +Murat.input.startLapseTime = 60; +%[text] Finally set the length of the coda window in seconds. The true lapse time at which we calculate the kernels is half of the window. The window is also used (after normalizing for its length) in the coda normalization method. +Murat.input.codaWindow = 20; +%[text] Set maximum travel time if you want to exclude traces beyond a certain value, else put \[Inf\]. +Murat.input.mintravel = 0.2; +Murat.input.maxtravel = Inf; +%[text] The **MLTWA** is the standard method to find the average parameters necessary to calculate the kernels. It provides albedo and extinction length: +Murat.input.albedo = [0.5 0.5]; +Murat.input.iExtinctionLength = [0.02 0.02]; +%[text] As the kernels are computational intensive and require a full matrix of nodes to avoid singularities, we also use a computational factor to reduce the computational time. This number divides the input grid, meaning that higher numbers give more precise results at the expene of computational time. Minimum is **1**. A figure will output the kernel in this grid. +Murat.input.kernelTreshold = 1; +%[text] %[text:anchor:H_865BAF76] ### Measurement of Qc +%[text] MuRAT3D implements either a linearised approach or a grid search approach to measure Qc. The linearised approach is the standard proposed first by Aki (e.g., [Havskov et al. 2016, BSSA](https://www.researchgate.net/publication/303510878_Coda_Q_in_Different_Tectonic_Areas_Influence_of_Processing_Parameters)) to best fit Qc after taking the logarithm of the energy. The uncertainties are derived from the simple minimum R-squared (fitTresholdLinear) and needs to be defined by a number between 0 and 1. It is advisable to set a minimum of 0.1. +%[text] The non linear approach models energy data measured on one-second windows across the envelope and minimizes the difference between data and model with a 1D grid search algorithm ([Napolitano et al. 2020](https://www.sciencedirect.com/science/article/pii/S1674987119301999)). Uncertainties are given by the experimental probability density function of the misfit. In both cases, uncertainties play as a weight in the final inversion. In the second case, leave the fitTresholdLinear = **\[\]**. +%[text] The user needs to choose between the two options **'Linearized'** and **'NonLinear':** +Murat.input.QcMeasurement = 'NonLinear'; +Murat.input.fitTresholdLinear = 0.02; +%% +%[text] %[text:anchor:H_7FEF6216] ## GEOMETRY AND VELOCITY +%[text] This section sets the details of the inversion grid and availability of velocity model. In MuRAT3D the coordinates of the model are in lat/lon, then they get converted in km. The vertical is in altitude above sea level. The velocity model can be 1D or 3D - if 3D all points must be given in lat/long formats. You start by setting the origin and end points of your inversion grid. +Murat.input.origin = [0 96 2000]; +Murat.input.end = [4 100 -30000]; +%[text] Then you need to set the number of nodes in the three directions. This is obviously dependent on the scale of your area. You will be playing a lot with this to test your effective resolution capabilities. +Murat.input.gridLat = 17; +Murat.input.gridLong = 17; +Murat.input.gridZ = 11; +%[text] You will see all of your figures on three sections cutting the models WE (degrees), SN (degrees), and horizontally (meters or km) at: +Murat.input.sections = [2.5 99 -10000]; +%[text] With this version of the code you are always using an underlying velocity model: the 3D is either unavailable (***0***) or a vailable (***1***) velocity model. For the 1D case MuRAT provides you *iasp91.txt*, the standard [IASPEI velocity model](https://academic.oup.com/gji/article/105/2/429/705789) and the more accurate [Lithos model](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JB010626), and expands any of them to a false 3D. However, a standard crustal model is generally available everywhere on the Earth, so use that, but change it to the same format as the file provided: first column is depth, second is distance from the centre of the Earth, then third and fourth are P- and S-wave velocity. Store the file in the folder **velocity\_models**. Here, we use *iasp91*: +%[text] ![Screenshot 2021-10-24 at 15.36.05.png](text:image:7a2b) +%[text] %[text:anchor:H_43D6D438] whose coordinates are Lat/Long/Altitude/Velocity in meters. So we set: +Murat.input.availableVelocity = 0; +Murat.input.namev = 'iasp91.txt'; +%[text] Even if we set the velocity model we still need the average crustal velocities if you have no info of origin time. It is highly recommended you have the origin in the haeder, at variables **'o'**! +Murat.input.averageVelocityP = 7; +Murat.input.averageVelocityS = 4; +%% +%[text] %[text:anchor:H_68B4738F] ## INVERSION +%[text] The code implements: +%[text] - a standard Tikhonov inversion based on singular value decomposition (**'Tikhonov'**), where we on the [regtools Matlab suite](https://de.mathworks.com/matlabcentral/fileexchange/52-regtools) from Per Christian Hansen. +%[text] - the particle swarm (**Particle**), simulated annealing (**Annealing**), or genetic algorithm (**Genetic**) solvers from the optimization toolbox. \ +%[text] In the last case, the user can choose the maximum number of iterations after which the optimization will stop. The user can also choose the maximum number of iterations where the misfit stalls after which the optimization will stop. Otherwise leave empty. +Murat.input.inversionMethod = 'Particle'; +Murat.input.MaximumIterations = 2e3; +Murat.input.MaximumStallIterations = 100; +%[text] Set this to ***1*** to plot: +%[text] - the L curves between residual and norm length ([Aster et al. 2013](https://www.sciencedirect.com/book/9780123850485/parameter-estimation-and-inverse-problems) - case **'Tikhonov'**). +%[text] - the decrease in misfit for the remaining cases. \ +Murat.input.PlotInversion = 0; +%[text] The user can set damping and smoothing parameters for Qc. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average inverse coda quality factor if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQc = []; +Murat.input.smoothingValueQc = []; +%[text] The user can set damping and smoothing parameters for Q. The first will increase the impact of the norm of the solution on the objective function. The damping is set to 0.1 of the average energy ratio if left empty, otherwise the user sets multiples of this value. The smoothing is set to three times the step of the grid in the vertical direction, otherwise the user sets multiples of this value. +Murat.input.dampingValueQ = []; +Murat.input.smoothingValueQ = []; +%[text] ### Synthetic Testing +%[text] A great reference for the best sort of testing is [Rawlinson & Spakman, 2016](https://academic.oup.com/gji/article/205/2/1221/692880?login=true). If you want to test you results you need to create a checkerboard. The size of the checks can be twice (*2*) or four times (*4*) the node spacing. +Murat.input.sizeCheck = 2; +%[text] In MuRAT3D you can also set the origin and the end locations of a spike as well as its attenuation value: +Murat.input.spikeLocationOrigin = [2 98.5 -10000]; +Murat.input.spikeLocationEnd = [4 99.5 -20000]; +Murat.input.spikeValue = 0.02; +%% +%[text] ## PLOTTING +%[text] Set to **1** if you want to plot the figures relative to: +%[text] *Rays* +Murat.input.PlotRays = 1; +%[text] *Tests* +Murat.input.PlotTests = 1; +%[text] *Results* +Murat.input.PlotResults = 1; +%[text] *Checkerboards* +Murat.input.PlotCheckers = 1; +%[text] *Spikes* +Murat.input.PlotSpikes = 1; +%[text] *Parameters* +Murat.input.PlotParameters = 1; + +%[appendix]{"version":"1.0"} +%--- +%[metadata:view] +% data: {"layout":"inline","rightPanelPercent":9} +%--- +%[text:image:3db7] +% data: 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9gXLhwgWr8vJyK82SI+3atStGmsZKOnXqlFVNTY0VAAAAAAC8NcFAAYAwwUABAAAAAIBGCQYKAIRJvTdQnHEydtUuq1e2nrIav+2k1cSyU1bjisutJi7bZFW8eYvVjh07rOINl3g5Y8Ydzxk3AAAAAADQMMFAAYAwwUDBQAEAAAAAaJRgoABAmNR7A8UZG4PWV1j1WVse0eqI+q6rtBqwtsJq0MZKq2c2HrcasiGiYZtPRFQcVfTzkGtl\/XpxxS4rZ6TkwtmzZ23dX3vtNTN58mQzc+ZMs2TJErNhwwZTUVFh3nzzzVrbyLQ5c+ZMSlVVVaU8vvZ\/9OhRe8xMzuX8+fNm+\/btZv78+bbOpaWl9lwy4cCBA2b58uVmypQpdh+rV682p0+fzqodFYrlzvvq1at8awEAAAAgLdI1ULJ9bw77vTcf9VP5pUuXmhkzZpiysjJz8eLF0Np206ZNZuHChbavo39LSkrMuXPnCnp+ddm+YfSbEpFun5B+FQZKozBQVN8uXbqYJk2aJNVDDz1kH7R+Bg4cmHI7p\/gvp774R44cMYsXLzbPPvuseeSRR7yyGzduTKvuxcXFMdv5NWnSpISmj5\/KymvtP2hQwu2bNWtmJkyYkNGXVWV79erl7WPr1q18awEAAAAgJwMljPfmsN97w66f8kJ27ty5Vt2aNm1qzY5s0ej9\/v37m\/vvvz\/huauuMmyS9RvCOL9cDZO67jcl47nnnkurP3jixAn6VRgotb+gUutFR6w6LTxq1bHosNWj849adV50zKrr0nKrHkuPxWpJuVXPFVEtO2bVY1m5VbfFx6w6vRaRM24y4dKlS\/ZBGf8wkVHSvXt307FjR\/PAAw\/ErBs+fHhoBorqG1Q2nQfBvHnzYuo8YMAA07dvX\/Pggw96y59\/\/nlz+fLlhNtrVEyHDh28stquX79+plWrVjF1efXVV9Nu02nTpsVsyxcdAAAAAHI1UHJ9b87He2+Y9Tt+\/Lhp3769t436IT179rTmiVs2d+7crOrmfz9Xn0Gd8iFDhphOnTrF1DOZSZPr+eVKXfeb6sJAoV9VxwZKdXW1bXSFomCgJL7RR4wYUStU58qVK2bv3r32Bu7WrZv9YgUZKAr9OXjwYKDiXU33INADctSoUfaLnO6DQA66nHKVVb3kWDt0fzzxxBPevpYtW5ZwH\/oPwpWR6+xmQVI9d+7caR5++GHPkU\/nYaLcNfFmFF90AAAAAAjLQMnmvTkf771h1k918I+GV0iRQz\/A+tcp5CQbA6VHjx5m\/fr1MSNsdFyFsvhH4Zw8eTL08wvTQKmrflO6\/co2bdqYFStWBCrdcCz6VfXAQFGD62Eh6YLk20CZsOes1YKKK1ZLKq9aLYpqcZwWVES09HhEC50qI3LbLYjT+N3VVu646aI2cDejRpnI\/EiH+GF9fgMlUwNHD27\/F3jOnDlpPwg0dM2VlTkTj+4R96B47LHHEg5Jcw9juduJUFygO4aGzCVDrn7btm1ruax80QEAAAAgVwMll\/fmsN97w36v37Ztm1d2\/PjxtdbL1HAjJZ5++ums6paMwYMHe8dfs2ZN6OcXBnXdb0rXQJEZkyv0q+qJgaJkSc5AyaeJ0hAMFJkgfqdx5MiRWZ9vLgZKPJk8CNyXKtmXdPTo0d7+FPMXT7t27by4x0QPVj3AtV7upxIuBaGHjHvwyozS\/cUXHQAAAADCMlByeW8O8703H+\/1fgMjyLzxlzl27Fiobe4Pb1Fy2bDPLx8Ust+kvqNMDSnIjArLQKFfVY8MFCHTJN8mijMyph2psZpffjVGcysiKiqPyBknRVHNC5BbPz8qt5\/ph2qsMjFQlHnZP\/okk1i0+mCgKFO2K6dEWEGsXLky6cPQH8Kk+yH+QeH+owly6h2Kl3T7KSoqMocOHeKLDgAAAAD1xkAJ6703H+\/1LlRDOVCC8Hekp0+fHmqbjx071tu3Zv5pTAZKGP0m9WfcOo1UyqeBQr+qnhkohTBRGoKB4n8AyeHLhbowUGT4uHKK\/wtC0575kyLFs3nzZm+9HtyzZ8+2\/4Eo7tMNddNwNn1xg9AwODfkTQ8luaZ80QEAAACgPhkoYbz35uO9XiNOXLlhw4YFltu\/f79X7qWXXgq1zZVXxO1beVIak4ESRr9JeWdSGUxhGCj0q+qpgZJvE8UZGVMP1ljNOnbVanpUc8ojmnksIvfZlXPLnUHils8uj8jtZ25UUw7XWGVioEycODFpnGF9N1D0sHdxkHLLg+L0Vq1a5e2vd+\/eCcvEZ3dWYiUlmXLZyTds2BBYDyVBUpygS5ikYW2CLzoAAAAA1CcDJdf33ny915eUlHjlFEYShGbpceU0e0xYyDDxT2ecKl9KQzNQwug3ydhQn09au3ZtUgNF5pxmN5JZo+u5ZcuWtM6HflU9N1DEnj178mKiNAQDxR9DmMuc6vEGyqOPPmofwvFK1yXO5EGk4YWu7KZNmxI+LPxlNG1bIvQQ6dOnT8JptvRAT4ZmLXIPCv\/9wxcdAAAAAOqbgZLLe2++Ovia9SWd\/COajceVUwc9DJQj0z+Nb6I+RUM3UMLsNyUj2TTG2rdmdaVf1cANFKEpe\/0mikyVsAyUyYdqrJwh4oyPWeURzYxqRlTTj0blykU1J6r45e5zNgaK3GZ3Iwa5iNkYKEHS8cJ+EPiHmbVu3TrmS6kpq+O\/xImGk+k\/Ef9onHg1b948cHYiZegOisPkiw4AAAAA9clAyeW9N58dfH+5WbNmBZbTyBBXrmXLljnXTzPa+Gd6USLZfBkYdW2ghNFvSseMkgmnET36gV4\/oLtwHHfNNIqIflUDN1CE4gGdgRLGsLWGYKA888wzMcl5wjJQxowZY\/cXL\/9c7mE+iF5++eWYWM7HH3\/cOpz6D8C508lyvcjlduvlemqomP5jUWJd\/0Mk\/j8Tffkfeughbwhh\/NTOfNEBAAAAoD4ZKNm+9+a7g798+XKv3KRJkwLLnTlzxivXuXPnnOqmvCsuaa76EEuWLMmrgVHXBkoY\/aZs0DXr169fzI\/q8SFE9KsamIESnwtFI1LCMlCmHKyxig\/RiQ\/hcfJCdZyiITvu84yo3PbOWMk1B4qyTodloBRyGmOHHvL+oXcuhlP78sc0vvLKKzHb+RNRyVDyf5kV5+ef5lkPGc1c5ND8825dr169zPDhw2PUv3\/\/mBhCLVPMKQAAAABAoQ2UXN578\/1e75\/hZdSoUYHlysvL00qGmo55olwnLmlutj+gNzQDJZd+Uy4o9Eo5Tdy+KysrY9bTr2pABko+zJOGYqD4Z+F56qmnGrSBIi5dumT27dtnVqxYYdtA2cTFggULvP3Nnz8\/ZhsNK3PrEv0noURGfsdUo2sc\/hCodPXkk0\/y7QUAAACAghsoubz35vu9\/tixY165oUOHBpZTP8OVGzlyZFZ1UuddISzaR9OmTXMa0dAQDZRs+025oll9gmY5ol\/VAAwU3SRyGvNhnvgNlOlHaqzmVFyN6FicosvdNMXOMHEhPu6zM0zmOrnkstFyUw\/VWGVioOjB6b8Jc3GZ64OBEoTf0YyvW9euXe1yDVvUgyRVO2l6M4eGQGq6tyB17949xqDSsilTpvDtBQAAAICCGyi5vPfm+73+woULXhiRRioEzRKjHCVuf5qCOVOU88SF7eh4mSSMbUwGSjb9plxRPygozw39qnpuoOTbPGkoBopiy\/xun27MoIdVQzVQ5DBrCGLQFMYuzk9l5LoH3S\/ZJKtSYl5i9QAAAACgPhgo+XzvDaN+\/hEKCilKhN7n3TmcPHky4\/r4R9jkOgtpYzNQUvWbckVhN66umeaboV9VhwZKIcwTv4Ey7VCNVVH5VStnhBRVROQZI1FDJH65+zy7PFbOcHEhP+44mRgorp7+USgaMqcYtWQoZjDera2PBsqVK1fMkCFDkn7Z\/Nmmg5JlKczLlRk0aBBfdAAAAABocAZKru+9O3fuNIsWLbKKD8EIo36aFSZZHhTN8OJGqSQK80lVP01o4favUS4ubKVQ\/ZZs2q8+9ZtyQWaXP+9KfA4U+lX12EDJV86ThmqgxD9Mpfbt29t4uMOHD9sHi4b4yTTRQ8fNGR\/\/0MrFQNF0ZNq\/07hx42JmB3LLjx49WmuEjDKH6zrGT4el\/3CUOdrtR9mcE7Fq1SqvjDI\/x0\/nrPvFP7VZJg87vugAAAAAEKaBkst7c67vvaNHj045xW0u9RMKz\/CPEHGzsSh5rKubRkkk6u+kqp\/LeyJ16dLFhpEEac+ePaGfXzrtV5\/7TcqZorwjkmZNSsTKlSut0eU3p1Qff9SD+pOZQr+qnhgo+TJP\/AbKxP3nrKYcvGg1\/VBEMw9fsnKfZxyOaGpUMw5diuhwRNOimu4U3W7KwQtWE\/ectcrGQNENPnXq1FrTl\/mzcMcvC9NA8SeDSiVNg+VHJo\/fSdY0XP7ptyQZRHrYBOFPqOX2o+zPivv0L9e0X3zRAQAAAKCuDJRc3ptzfe9NxwDItX5aJnPDH0akH3f9\/RL90JuIVPVL1KcJUnyOjjDOLwwDpS77TaWlpV65GTNmJCzj2lgzGyk9hPbvb3fln3njjTcwUBqSgSKzQC5YdXV1XivYkAwUh9xCfZGUjTroi6j51ufOnVur7TXEz5XR8LtM8A\/XS6X46yY3Osj4kcs8c+bMlHlddE9oKF2rVq0S7kf\/oehLm2l+mHXr1nn7KCsr41sLAAAAADkZKLm8N+f63jt27FivnEYUhP1e79BICeXgiDc8VGe9XweRqn5BfYZEUmhM2Ofnr5\/MhWyoy37Ttm3bUhpMQftv3ry5\/cH+3LlzWZ03\/ao6NFAKhYZ9SaO3v241fu9Zq4l7z1lNuPa3NHHfOasJ+yOatC+iCXtjNdHTWatJ+yKauLfaavS2163ccXNBMXCaSkxxdIsXLzZr1qyxMXsnTpyol22tECM9TBRipKm3NNwwm6RSSqaloWn6T0P7Ua4cF8YEAAAAAFDXBkpYNIT3XhkpqpfCQtQ3yXayi\/qEfoiWCTBixIgG3W8KQvtSv1HhYu6+0o\/0mmUJMFAarYECAAAAAAB1R74NFKgbZJzIQFE6CYBCUu8NFOVXkUas3G41suyM1ajtVRHtqrYasaPKauS1v622R7UjolG7IxpxbRtp9M5qqxE7orq2T6sV263ccQEAAAAAoGGCgdL4UAiMcoMor0ums9AA5AoGCgYKAAAAAECjBAOl8aEcKMo3GWbIDEC61HsDRXFekjM0XGiNS\/Ialtx+3XHccQEAAAAAoGGCgdL4cFMyA9QFGCgYKAAAAAAAjRIMFAAIk3pvoAAAAAAAAGQDBgoAhAkGCgAAAAAANEowUAAgTDBQAAAAAACgUYKBAgBhgoECAAAAAACNEgwUAAiTBpNEtry83Gr37t1W8Ulg9+\/fb3Xq1CmrmpoaKwAAAAAAeGuCgQIAYYKBAgAAAAAAjRIMFAAIk3pvoDjjZOyqXVavbD1lNX7bSauJZaesxhWXW01ctsmqePMWqx07dlilmsbYGTPueExjDAAAAADQsMFAAYAwwUAJyUA5cOCAWb9+vdWlS5ca7A3RWM4DAAAAAAADBQDCpN4bKM7YGLS+wqrP2vKIVkfUd12l1YC1FVaDNlZaPbPxuNWQDREN23wiouKoop+HXCvr14srdlk5IyVdXnjhBdOkSROrysrKBntDNJbzAAAAAABI10B58803zdGjR82SJUvS6gPoh9YzZ86kVBioPkuXLjUzZswwZWVl5uLFixltrx9Ily9fbqZMmWJmzpxpVq9ebU6fPh1K227atMksXLjQTJ482f5bUlJizp07l\/G+Mm3\/sMn1+IWqv1JUuHvr6tWrScuqj79161ZTVFRkr\/2iRYvMxo0bzdmzZ3kwYKBgoGCgAAAAAACkZ6Cow3vkyBGzePFi8+yzz5pHHnnEewdWJzMVzz33nFc+mU6cOJF13ZXXsXPnzrX22bRpU2tWpELv8oMGDUpYr2bNmpkJEyak7IQnQqP3+\/fvb+6\/\/\/6E+1ZbyvBRGyczHHJp\/zAMk1yOXxf117Xq1auXdxyZI4mQYdayZcvAe1LXXoZKsusDDdhAcSE2rRcdseq08KhVx6LDVo\/OP2rVedExq65Ly616LD0WqyXlVj1XRLXsmFWPZeVW3RYfs+r0WkTOuMFAAQAAAABoXAaK3vODOpj1wUA5fvy4ad++vbefjh07mp49e1rzxC2bO3du4PZVVVWmQ4cOXtkHH3zQ9OvXz7Rq1Sqmfq+++mrGdZs2bZq3\/UMPPWQ79UOGDDGdOnWK2XcykyfX9s+VXI9fF\/X3t3syA2Xnzp1eGRlwgwcPttdH95B\/+1mzZvGAKKSBUl1dbS9aRUUFBkoIxsOGDRvM0KFD7YPx8OHDGCgAAAAAAHk2UNSpHDVqlBkwYEBWBkqbNm3MihUrApVpuI3QyIAuXbp49VHIjUPhF\/51paWlCfchY8SV0WgQNzup9q0O9sMPP+yNRrh8+XLGHfkePXrYnIn+ESza9\/z582NGOpw8eTKpAZFt+4dloGR7\/ELXXzk940f8BBko6sMpVEsmnB9dK5km\/pFCUEADRRdMX0ZJFzTfBsqEPWetFlRcsVpSedVqUVSL47SgIqKlxyNa6FQZkdtuQZzG7662csctlPEwadIkb3s91DBQAAAAAADyY6DIUFCIjGPOnDlZGShPPPFE6HXetm2bV5fx48fXWi9TQiNKtP7pp59OuA9nsujH2USog+2OoVCUTHBmTBAa8eD2vWbNmsB95NL+uZLr8QtZf40matu2ba2RLkEGSjJkcvlHomjfUCADRbFVzkDJp4nS0A0UucRKrrRly5aED285gbpxx44d622v8lomxSdhkovt1vnj1rRvucBBo1f0JdfDeO3atUlHuGCgAAAAAEBjN1DiqU8Git+ACDI3\/GWOHTtWa327du28UQaJDA8ZM1qvUQ3nz58Ptf7z5s3z6qbksulQaAMl7ONnsr3r\/0mpzCj199y1fuCBB2y\/OxcDRbg8Krr22YyQwkDJIQeKTJN8myjOyJh2pMZqfvnVGM2tiKioPCJnnBRFNS9Abv38qNx+ph+qscrVQJFJIcfXP9RKN318rNn27duTxk0+9thjMeVffPHFmONotIoe3P5t5Cpqv0JTEQ8fPtxzqZ30RUz0sMRAAQAAAAAMlLoxUPTjqes\/KAdKEP6O9PTp0wPr50J44jvwzmAJGqGSC\/4fhjVzEAZKLDI+XFmNBEqG8si4sppN59ChQzkZKFeuXDEtWrSw22uUEhTYQCmEidIQDZR169Z5cYWJtHnzZm87TUWWiYHiP87KlStt4qZE2+mhKFczKPO2NHHiRAwUAAAAAMBAqScGikacuHoMGzYssNz+\/fu9ci+99FKt9epvuPUyZGbPnm2NE+U70awxLkeJOuRh4w8R0Qh5DJRYlLcmHYPp4MGD9hqpnPp0Go2Sq4EyZsyYjK8NhGyg5NtEcUbG1IM1VrOOXbWaHtWc8ohmHovIfXbl3HJnkLjls8sjcvuZG9WUwzVWuRgoTn379rVzrms\/ynzslg8cONDbTg8yzePtd2r1pXPze8eH8CQ6zujRo72kt926dYvJiu2yL+u6aFpmf\/ZmJb3CQAEAAAAADJTsDBSZE5p9RklE9U6usP1cKCkpiXnHD0IJQv19jkTEz9qifoKSv7pZeTSJRdioU+5PUpoqROWtaKDIGFF\/UFJ6hUQotEY\/pLs+m8tVkomBor6kwrsUmaDpll3UgmZyUn2hDg0UsWfPnryYKA3VQIl\/4PmH48mVjSfdJLLxx1myZEnMen2Of1AqD4vfsHHDtqT4uDcMFAAAAADAQEnPQEkkhcXs3bs3q\/ouW7Ysrfwher935WTgJEIjFvr06ZOwjjJqwkY5Mv1pA5TXsVAGRkMyUNJhxIgRnkHn71dnYqD07t271nXXiJZ9+\/bxYKgPBorQlMZ+E0WmSlgGyuRDNVbOEHHGx6zyiGZGNSOq6UejcuWimhNV\/HL3OQwDRQ9U\/5RejtatWwdOGZWNgZJobnU9uNx6xbX5zROHXGpXJn49BgoAAAAAYKCkNgtkQmjEhd7JFUbjwi2kli1b1ppGNtOOeHzuRD8a2eE\/ViLzROH6QSZP8+bNzWuvvRZaO2tGGv9MMUokW5cGREM2UDRzUVB+m0wMlOeff96OXomf\/lgml6a5znT6asiDgSIUb+cMlDCGhTVEAyXIeHDxgGEZKImOo+S1br3mJE+Ef55yDBQAAAAAwEDJvQOskIl+\/frFjAT3z5qZDgr\/d9urf5DsWK6cwvXj0egVt16jGRQCIkNFk1r4O9NhmCjK2+KS0qqzHj9CHgMlfWS6uTQM+tE7\/kf5bHKgaB8yuFRHGSr+SUUyvT8hZAMlPheKRqSEZaBMOVhjFR+iEx\/C4+SF6jhFQ3bc5xlRue2dsYKBgoECAAAAABgo2aB3a38nNdP3af8MLUHv8kK5DV05vdv78SeYfeaZZ2I6ycq\/4Z\/BU4aHm70zW\/NEfRsXHpLtD+gYKBGefvppbz+ablizqfrVv39\/b71CdLRMuW7SRfejf7KT4uJiHhJ1ZaDkwzzBQMFAAQAAAABoKAaKUOhEtjOdKOmn23bo0KGB5TR5hCs3cuTImHUKJ3LrEpkjyoHoHymjmVmyQX0Fl6JAiUmzmRUGAyUW\/4Qg6erJJ5\/M6BiaOjmdRMWQJwNFsVNyGvNhnvgNlOlHaqzmVFyN6FicosvdNMXOMHEhPu6zM0zmOrnkstFyUw\/VWGGgYKAAAAAAAAZKpkyZMiWtPCaJuHDhghdmo5EsQSEWyjHijqEpiv107drVLtd+Ll26lHB7GStu+0QTXKRCISEubEfHySRhLAZKMAq90jTTQerevbt3nKeeesou0\/2WCRp14vahES9QQAMl3+bJW9VASZYVGwMFAAAAAKD+GigKqwiaMTMd\/CNYFHKTCDfDikJwTp48GbNOs\/K4dfEzbvr7ccmS0KbCP4Il0cQWGCj5QdMeZ5oDJR7\/TE+MQCmggVII88RvoEw7VGNVVH7VyhkhRRURecZI1BCJX+4+zy6PlTNcXMiPO06hDRS50257JXjCQAEAAAAAaFgGiswM\/1S+id6nNdp80aJFVolCfDQFcrL3ec0A5EapJArz8U+xHJQkVukXXJlBgwZlVL\/Vq1d722qUTBizuWTS\/qnqh4GSvA\/vH8WyePFiHhKFMlDylfPkrWqg6Mvvtm\/fvr3Ztm1bwiF7GCgAAAAAALkZKJoGWAlQncaNG+e9AxcVFXnLjx49mvCdfOXKldbI8JsHKu\/PYdGnT5+EddKv\/q6MEromQuEZ\/hEebjYWJY910wVrhEmi\/sqqVau8bTWjizrd8f04\/5TD8SZEqvq5vCdSly5d7A\/BQdqzZ0\/C88ul\/dNpv1Tkev1z2X7fvn02b4mkWZfCNlA0DbJCgXR\/+lGff8iQITEjj06fPs1Doi4MlHyZJ34DZeL+c1ZTDl60mn4oopmHL1m5zzMORzQ1qhmHLkV0OKJpUU13im435eAFq4l7zloV2kDR8LoOHTrEJARq0aKFHZ6HgQIAAAAAEJ6B4k\/CmkqaMjgemRdu5hn9oq+wGbdMUn6QN954I2sDRceUOeHv7OpHVv\/sOStWrAg8b38iWTdSRLO6uP6I08svv5xx\/fznmUpBOWByaf8wDJRcr38u25eWlnrrZsyYEbqBon26Mppxp0ePHvZeUqJff71yzVuDgZIhclvlqlVXV+e1gg3FQPE\/pDR\/dyJcPKJc20TETysm6YbP5Di6Jqkyavunxzp37lzG5wEAAAAA0JANFH+YTCol6u+4EJp4NW\/e3EydOrXWO7afsWPHeuU1YiWIqqoq+2NqvGHRqlUrs27dupR9NYW4qGyiespIUWc80eiKVPULOvdEUmhLInJpf3\/9ZF5lQ67XP5ftFWmQbZJhXXe3bVlZWcIyCxYsSGpy6Z5S\/aHABkqh0LAvafT2163G7z1rNXHvOasJ1\/6WJu47ZzVhf0ST9kU0YW+sJno6azVpX0QT91Zbjd72upU7bqHR8DyN6FFmbBkqQYmfAAAAAAAgOwMlV5TrRLk4FC6jDqtyQypkQrPohI2MFO1fYUOa5jhoZp5EqC+hkBGZJa6eGrEeRt6SumTu3LnWDBgxYgQ3eQJ0H2qUjK67jDQlM9aIFX4gzx0MlHpmoAAAAAAAQDjky0CBukXGiQwUpZMAKCT13kDRaAxpxMrtViPLzliN2l4V0a5qqxE7qqxGXvvbantUOyIatTuiEde2kUbvrLYasSOqa\/u0WrHdyh0XAAAAAAAaJhgojQ+FwCj3jPLCkLMRCg0GCgYKAAAAAECjBAOl8aEcKD179rRhVACFpt4bKIrfkpyh4UJrXJLXsOT2647jjgsAAAAAAA0TDJTGh5vSGaAuwEDBQAEAAAAAaJRgoABAmNR7AwUAAAAAACAbMFAAIEwwUAAAAAAAoFGCgQIAYYKBAgAAAAAAjRIMFAAIEwwUAAAAAABolGCgAECYNJgksuXl5Va7d++2ik8Cu3\/\/fqtTp05Z1dTUWAEAAAAAwFsTDBQACBMMFAAAAAAAaJRgoABAmNR7A8UZJ2NX7bJ6Zespq\/HbTlpNLDtlNa643Grisk1WxZu3WO3YscMq1TTGzphxx2MaYwAAAACAhg0GCgCECQYKBgoAAAAAQKMEAwUAwqTeGyjO2Bi0vsKqz9ryiFZH1HddpdWAtRVWgzZWWj2z8bjVkA0RDdt8IqLiqKKfh1wr69eLK3ZZOSMlXWS2nDlzxtPly5fT2u7ixYsx2wVx4sQJs27dOjNjxgwza9Yss2HDBnPkyJHAMCV\/fa5evZqyHq7suXPn+FYAAAAAQKMgmYGi5Zs2bTILFy40kydPtv+WlJSk\/T6sPtTWrVtNUVGRmTJlilm0aJHZuHGjOXv2bOjn8eabb5qjR4+aJUuWZNRHESq\/dOlS248oKyuz\/Y+w2jaX9gvr\/Oq6\/vlq30So75duHy\/M6wMYKKEbKAMHDjRNmjTxNGfOnLS2Gzp0aMx2J0+erGWEjB8\/3jzwwAMx5Zzuv\/9+M3r06KT1Ufsl4\/Tp017Zzp07860AAAAAgEZroGj0ef\/+\/e17dKL360ceecR2iNWpT8SBAwdMy5YtE24rNWvWzBoqQdunayjox9LFixebZ5991tbJ7V8mTTooL6Te7ePr17RpU9uZzpZc2y+s86vL+uezfYOQYdKrVy\/vODLv8nl+0EANFBdi03rREatOC49adSw6bPXo\/KNWnRcds+q6tNyqx9JjsVpSbtVzRVTLjln1WFZu1W3xMatOr0XkjJtsDZQuXbqk3Ka6utp+wYIMFN3UgwcPjln\/0EMPmSeeeMI8+OCD3jKZMLkYKPryY6AAAAAAwFvBQJk2bVrMu7U6pUOGDDGdOnWKee8O6gTv3Lkz5t1Z7+vavmPHjjHba9R4tuj9PcigScdgOH78uGnfvr23jerWs2fPmL7H3Llzs6pbru0XxvnlQhj1z2f7plPvZAZKGOcHeTBQ1PnXRauoqMBASWCgSIcOHUq6jYaoxW\/jN1DWr18f86XUkCsXGiQHUrldxo4da0eoYKAAAAAAAKRnoPTo0cO+a\/vDIPTj5fz582NGksSPDheVlZVm5syZthPtR\/uSaeL\/pT9XA0V9gFGjRpkBAwakbTDoPPRjriu\/evVqb53Ci\/zrSktLs+rI59J+uZ5fGEZELvXPd\/smQv2++BElyQyUXK8P5MFA0QXT0B9JFzTfBsqEPWetFlRcsVpSedVqUVSL47SgIqKlxyNa6FQZkdtuQZzG7662csfNxUB59dVXk26jGzuZgfLSSy95y7dt25ZRu2GgAAAAAAAGSm0DJSiHoMM\/AnzNmjUZHU+dVP9IlKqqqqzqrTrqHd2h9ADpGgzqN7iyiX5oVX\/DjWZ\/+umns6pbru2Xy\/nlSq71z3f7xqN7qG3btrX6jUEGSj7vb8jBQFHsnzNQ8mmiNGQDRTd6UHIf5VdJNGTNb6D07t3bW66ERBgoAAAAAAC5GSipmDdvnvdurOSbmeLyVGjEQFgJRTMxGPwdZOUZSVXm2LFjobZ5Nu1XSAMl1\/rn2r7qH8oUkVKZHf6UDsqJqX53KgMl3\/c3BkoOOVBkmuTbRHFGxrQjNVbzy6\/GaG5FREXlETnjpCiqeQFy6+dH5fYz\/VCNVbYGyqOPPmrj39xNuX379oTlp06date3aNHCxqQlMlBeeOGFmDwnmST6wUABAAAAAAyUzA0Uhci7d2PNrJIJV65cse\/36eZEDNtA0QwrLtRDOTqC8HfEp0+fHmqbZ9N+9clASVb\/MNpXxodbp1CwZChPiSur2Z6UIiJXAyWX+xtCSCKbbxOlIRooyibtbsoRI0bUKisjxCUd0gw6zz\/\/fEIDRfF0\/tEpzz33XNrTomGgAAAAAAAGSuYGij8ER3kkMmHMmDFZbxuGwaAREa7csGHDAsvt37\/fK6e0AWGSTfvVJwMlWf3DaF\/lRUnHwDh48KDNU6JygwYNsn3IMAyUXO5vCGkWnnyaKM7ImHqwxmrWsatW06OaUx7RzGMRuc+unFvuDBK3fHZ5RG4\/c6OacrjGKhcDRfNyu2mH5UDHD83SqBR30+oYQQaKviR9+vSJMVFatWplVq5cmXI0it9AWbVqlR06FiR\/HB8GCgAAAAC8VQ0U\/yQOSgKbLMRC7\/x6l9a7vX5A1UyZbhpbGQJhkq7BoEknXDn9UBuEEuC6cn379g2tnpm0X300UFLVP4z2lTGivpq0du3ahNsr9Ouxxx6z27dp08bLpZOrgZLt9YGQDRSxZ8+evJgoDdFAEXIJg5w9jUpxWadlhAQZKOLChQsJ86t0797d5qFJx0DJRBgoAAAAAPBWNFD0bu2Sf0qbNm1KWt6fr9A\/s8m+fftCP490DYZly5alld9Co9pdOU1vGwaZtl99M1DSqX+h2tf1FxUu5O9X52Kg5HJ9IA8GitCUxn4TRaZKWAbK5EM1Vs4QccbHrPKIZkY1I6rpR6Ny5aKaE1X8cvc5LANFbqK7OZ955hmvnNxEzcftH7KVzEARMlkWLVpkmjdvHvOA1igXTZWWaDQKBgoAAAAAYKCkZ6AopN0\/04kSbaZC7\/AaHRA\/vaw6qZqN8\/LlywU3UPzl1E8IQiMPXLmWLVvmXL9s2q8+GSjp1r8Q7auZcYLyp2RroOR6fSBPBorYvHmzZ6Bs2LDhLWug+I0SGR3V1dV2uT+viZs7PpWB4tB18ieWdXr55ZeTGigaAaMvTZDcPOwYKAAAAADwVjNQlNeiXbt23i\/+S5YsyegYmlVF79TqXMtQce\/Vmj0lk0kgwjAYli9f7pWbNGlSYDmFH4X1\/p9r+9W1gZJJ\/fPdvuofuj6kQn\/iZ3TNxkAJ4\/pAngyU+FwoGpESloEy5WCNVXyITnwIj5MXquMUDdlxn2dE5bZ3xkpYBop48cUXvRtc7SEGDBhgP2vInyNdA8WhOEt\/8p9E0xyTRBYAAAAAMFCSGyjqXCoXhAu\/yfUH4MrKSvPwww9779bFxcUFNVD8M7yMGjUqsFx5eblXTv2TXMyHMNqvrgyUTOuf7\/Z9+umnve00Hfbw4cNj1L9\/f2+9+pNaNm3atILd3xCigZIP86ShGyj+L5i+AKdPn\/aG+Mm9zNZAEXI1\/SaKvjwYKAAAAAAA6RkoMjtat27tJX7NdlaTeDQ1bTqJRvNhMCiprSs3dOjQwHL+EegjR47M2iwKq\/3qwkDJpv75bt9u3bplnILhySefLOj9jYGSo4Gi2D45WfkwT\/wGyvQjNVZzKq5GdCxO0eVummJnmLgQH\/fZGSZznVxy2Wi5qYdqrMIwUDTkyt207gHq4iI1h3guBkr8w1lfNgwUAAAAAIDUBoref11Yg8Ltw0yoqVEn7t1aIwoKaaBoAgo3G6jCiYJCiJQDw+1v9uzZGdcn7PYrtIGSbf3z3b5KTPvss88GShOJuP0+9dRTdtmUKVMKen9joORgoOTbPGnoBooYP358LZdQhomfbA0U\/\/TDmjYNAwUAAAAAILWB0q9fP+\/9d+HChaEe0z9TS6FHoMT3LTRlbiLcDEIaHZ9J\/yNf7VdoAyWX+heifYPwT1SSbERJPu9vDJQsDZRCmCd+A2XaoRqrovKrVs4IKaqIyDNGooZI\/HL3eXZ5rJzh4kJ+3HHCMlD2799fy0ApLS1Ny0A5evRo0sRTchvddmPHjsVAAQAAAABIYaD4J3XQKIIwZ8vRvvyjBBYvXlyrzM6dO+0Mm5ImewjbYNi7d2\/SPB2aztaNokgUhpKqfvlov0zOL5v2C7P+ubZvvg2UfN7fkIOBkq+cJ43NQBGPPfZYzE0cn005yEDp0KGDzXOiUB3F2125csV7MC9YsMD7YsrZ1BcZAwUAAAAAILmB4g+x79Kli52ONkh79uyptU9NM6tQC3WU\/ahPNWTIkJjpa5UDMR4X1p9oFLlD0+AqAajTuHHjvG2Kioq85UE\/uCq8wz8CwfU\/lNzUTWerPkSi\/k6q+uXafrmeXzrtl4ww6p9L++7bt8\/mLZH8eTHDMlDCOD\/Is4GSL\/PEb6BM3H\/OasrBi1bTD0U08\/AlK\/d5xuGIpkY149CliA5HNC2q6U7R7aYcvGA1cc9ZqzANlNdee81mTJYSDaFKZqD4R67IMGnfvr1NAuRfrodMUH0wUAAAAAAAA+VPuEkd0pE6mfHMmDHDW68Zd3r06GE7qvHv6EF5J9IxAPxJSFNJk0vEo2Wqk9\/MUT\/CfVYbrFixIqv65dp+uZ5frgZKGPXPpX0VjeDK6V4K20AJ4\/wgDwaKRkHIFayurs5rBRuKgTJo0KCsDQj\/dMcyM\/zGipsHPJH0sI6fvji+PlL86JR4dP39LiUAAAAAQGM1UNwo7nSk0JJ4NBI8WSdV+S+SvX8r9D5oIgiHP0wklYL6Y1VVVbYu8XVt1aqVWbduXdb1y7X9cj0\/f\/0ULpUpYdQ\/l\/b157HM1MDQft22Qf3AsM4PQjZQCoWGFUmjt79uNX7vWauJe89ZTbj2tzRx3zmrCfsjmrQvogl7YzXR01mrSfsimri32mr0ttet3HHrGg0HO3z4sHWwlyxZYkezlJSU2FE\/yfKjAAAAAABgoARPY5wLmo1Foyg0IkC5OPSerhEBx48fr3dtoI6+cleuXLnSpgVoDH2IuXPnWgNgxIgRtC8UFAyUem6gAAAAAABAduTLQIG6RcaJDBSlkwAoJPXeQNFIC2nEyu1WI8vOWI3aXhXRrmqrETuqrEZe+9tqe1Q7Ihq1O6IR17aRRu+sthqxI6pr+7Rasd3KHRcAAAAAABomGCiND4XANGvWzOYdqayspEGgoGCgYKAAAAAAADRKMFAaH8qB0rNnz5jJNwAKRb03UBRfKDlDw4XWuCSvYcnt1x3HHRcAAAAAABomGCiNDzdlMEBdgIGCgQIAAAAA0CjBQAGAMKn3BgoAAAAAAEA2YKAAQJhgoAAAAAAAQKMEAwUAwgQDBQAAAAAAGiUYKAAQJhgoAAAAAADQKMFAAYAwaTBJZMvLy612795tFZ8Edv\/+\/VanTp2yqqmpsQIAAAAAgLcmGCgAECYYKAAAAAAA0CjBQAGAMKn3BoozTsau2mX1ytZTVuO3nbSaWHbKalxxudXEZZusijdvsdqxY4dVqmmMnTHjjsc0xgAAAAAADRsMFAAIEwwUDBQAAAAAgEYJBgoAhEm9N1CcsTFofYVVn7XlEa2OqO+6SqsBayusBm2stHpm43GrIRsiGrb5RETFUUU\/D7lW1q8XV+yyckZKushsOXPmTC2dPXuWuywHLl686LXlm2++SYMAAAAAQNqka6DoPfPo0aNmyZIlGfUBxIEDB8zy5cvNlClTzMyZM83q1avN6dOnQz2PXOqn8kuXLjUzZswwZWVl9v06rLbdtGmTWbhwoZk8ebL9t6SkxJw7d66g51fX7etHKSRc3+Xq1auh1zPT\/Z8\/f95s377dzJ8\/396bpaWl9E8xUOqHgTJw4EDTpEmThGrRooXp2bOnfZjm44vUmHnhhRe8dqysrKRBAAAAACBnA0Ud5iNHjpjFixebZ5991jzyyCPeO+fGjRvT2rfeTQcNGpTw\/b9Zs2ZmwoQJWb\/7h1E\/5YXs3Llzrbo1bdrUmh3ZotH7\/fv3N\/fff3\/Cc1ddZdgk+\/EzjPPL1TAJ+\/i61r169fL2s3Xr1lDrnOn+i4uLY87Lr0mTJvHjdGM1UFyITetFR6w6LTxq1bHosNWj849adV50zKrr0nKrHkuPxWpJuVXPFVEtO2bVY1m5VbfFx6w6vRaRM27CMFD8Gjp0KHcdBgoAAAAA1KGBovf8oPf1dDrQVVVVpkOHDt42Dz74oOnXr59p1apVzL5effXVrOqda\/2OHz9u2rdv723TsWNH+4OuzBO3bO7cuVnVbdq0ad4+HnroIdupHzJkiOnUqVNMPZOZNLmeX67k4\/j+dsmHgZLJ\/ufNmxdzjQYMGGD69u1r71O3\/PnnnzeXL1\/mIVEoA6W6utpetIqKCgyUOANl5cqVdiifhkjpwfHEE0\/E3OxaD+mxYMECM3jwYKs33niDBgEAAACA0AwUGQujRo2yHcxMOtAyRlx5jbZws3\/qV\/2dO3eahx9+2BuJkk0nNZf6qQ5dunTxymsUvEPhG\/516q9k05Hv0aOHWb9+fcwIGx1XoSL+UTgnT54M\/fzCNFDCOr5ybsaPyAnTQMlk\/\/rRWW2vMt26dbMjkRzq9\/v7psuWLeMhUSgDRRdMDwtJFzTfBsqEPWetFlRcsVpSedVqUVSL47SgIqKlxyNa6FQZkdtuQZzG7662csfNxkDRdMp+rly5EvOllNsHAAAAAAB1Y6DI8PB3LOfMmZNRB9qZEBrVkQjlm3D7U6hIpuRSv23btnllx48fX2u9TA03EuHpp5\/Oqm7J0A+f7vhr1qwJ\/fzCIMzjazRS27Zta41kCctAyXT\/CklyZQ4ePFhrvfr+zmB57LHHCOUplIGiERbOQMmnidIYDBShBEtuvZzAZMjJlXOobST9nerGVjIofbkkf1n9hyF3+PDhw4EPDz1k165dG1jGjxIR6fxUL42k0bbJEhEF1UvbaB9btmxJmtgraPtc6wUAAAAAb10DJZ5MO9Dt2rXz8n0kMhRkXGi9Rg3oPTVXMqmf38AIMm\/8ZY4dOxZqm\/vDR5RcNuzzywfZHl\/9E9eWDzzwgO0XpzI41Ndz\/ZtUZlQ2+3dmi0aaBDF69GhvP8qVAgUwUIRMk3ybKM7ImHakxmp++dUYza2IqKg8ImecFEU1L0Bu\/fyo3H6mH6qxCttA0Y3p1ivpUtCXSV9ef2yaP3ZNQ+KCTIQXX3wxJleIhg7Ghw5piJqyMItLly6Z4cOH1zqWvqCJHvLKqC2H2h836U9Epaze6dRLJo2cev8QND0MZs2aldb2YdULAAAAADBQsu1AP\/fcczEhPPHv9M5gCRqhkq8OvmbAce\/ZyoEShL8jPn369FDbfOzYsd6+030Xb6gGitI1uO2KiorMoUOHUhocWu7KaKRSmPvX9XfrleA4CP3gnKnJBSEYKIUwURqLgTJx4kRv\/SuvvFJrvUZa9O7dO8YAaN26tZV\/mZJTJXIq\/clW9YWQ4ZIoKZIe5nI7gzKGS6prsv3L8JAZo3\/92yVyL\/3brVu3zosHTaTNmzcn3T6RgZJtvQAAAAAAAyXbDrTeW115GRazZ8+2xonynbgQCoVJqMNbyA6+Rpy4csOGDQssp\/6KK\/fSSy+F2uZ6H3f71kj4xmqgKDzGhcKob6UfutMxUJR3Jh2DKZv9nzhxwluvFBJBaDpr0kvUkYGSbxPFGRlTD9ZYzTp21Wp6VHPKI5p5LCL32ZVzy51B4pbPLo\/I7WduVFMO11iFaaDo5nadev2baJjc1KlTve01csQ\/hbLK+5M9JRqt4TcSnDQ0yyXDVdiQfzSL\/tW0ZrpeOpY\/q3ObNm1q7X\/MmDH2y6U2ccmiZMRoebIZhhLVSxmgly9fbveljN1uudowUwMl23oBAAAAAAZKLh3o+FlR9L6t5KpuVp4NGzYUvIOv0dn+vkAQmqXH\/24eFjJM\/NMZpwpRaagGin78Vv4Q13dS\/0OkY6DIGFG\/R1IahTD3r\/6QizDQD+dB0QurVq3y9qMf8aHABorYs2dPXkyUhmig6Pw16mHRokUxyWPlTmvoVTxK5OTcRd3wmuEoHn1p3JdB\/8Zfs3ijYsmSJTHr9Tn+Ae\/PEaIvW4sWLbz1+tKmg\/\/hK5MnlYES\/yD3DzOUW52pgZJtvQAAAAAAAyWXDrw6p3369Ek4slpGRl108DWrSjqhGeoHuHKafjgMlCPTnx5AuQnzZWDUtYEyYsQIr3\/n7\/emY6CkQy77V9hYsmugfp+\/jKbjhjowUISmNPabKDJVwjJQJh+qsXKGiDM+ZpVHNDOqGVFNPxqVKxfVnKjil7vPYRgoifToo48GtodGY7hyihkMwp\/sxz8dWbzRkGjOdT3Q\/IZCogSrcp9dmUwSsLowI7mcyQwQxYr6pzqL314udVgGSqp6AQAAAAAGSrYdaJkn\/hD9eDVv3ty89tprBe\/g+8sF5RgUGhniyrVs2TLn+mlGG\/9MMUokm08Doy4NFM0sFJQ\/JgwDJdf9+8Nz1B\/au3evt04\/1Pvz96RKNgt5NlCE4gGdgRLGsLXGYqAki2+bNGmSV05mShB+R1lDBjMxGpS81a3XXOeJ8I+WSWagaHSHnFANOdMoG\/8wsWwMEBcnmauBkkm9AAAAAAADJdsOvEZ3uPIaLaDR4jJU4nPxhWWipFs\/\/w+z6mMEcebMGa+cwvpzQXlXXNJcjZiIHwnfmAwU9TdcOgT9+Bz\/43CuBkpY+3\/55ZdjoiAef\/xxO+pExp4bdeSfRATqyECJz4WiESlhGShTDtZYxYfoxIfwOHmhOk7RkB33+f+zdx5eUVzvG\/9vU21RE2P7WsGuQVHTRNEooKJgQUBEFDT2imikCAgLlpyogAqRzM\/nzr7zuzvM7E5dtjyfc54T2Z22d2cnc595S31Ssr4YK1EYKBiHDx8+pHTewcUUUSBOoEaHlx8CWv7Kcuigk00DBT9eGDjr1q1zNYlmw0AJelyEEEIIIYQGSpAJvF6AddeuXSl1JlDfQu+EicmrdMHMxgRf7\/Dids8PUAPRS7FRL+YJ7uGlaG7QB+j5YqCg+6cst3HjRjUn04Vuq3ptEbxmf\/Cdjii3D\/PO3nEVf+Oz6rVqnBqckCwYKHGYJ\/lqoOhFZHHhktdRCMiptohU6s6UL6kbMjAWsmmg6CGKaA+MHzScd1wkEfY3WwZK0OMihBBCCCE0UIJM4NG1RpZ1Mkdwv4\/OmbIMmhtka4KP5hNeGimgyYQst3\/\/\/kDHhHtzSZnHfXiYmh\/5YqDojTm8av369Z6PI+rtT05OGr29vUZ7e7ua26JLFGhubrbWb2pq4oUimwYKvgRMVuMwT3QDpe75lNLZkWlTwzYlX5c2xWKYSIqP\/C2GSYNIissmlzsxMKUUlYHy\/v171YM9nXlRW1vrKYVHLwRr7xkep4GiV2nGMoiu0UFl6NkwUMIcFyGEEEIIoYESZAK\/Zs0aK8IcE1QnYKzI9pwaJcQ1wcf9sKQR4V7YrQsLapTI9tCC2S+oeSJpO9ifn4Kx+Wyg4EEtHn67SY+K37Bhg3oNcz2vxL19QY90gZlGsmSgxG2eFIKBAjo7O1NcQvuPsqOjw5MDjHa9spy95VWcBgr6jsvr6BhkZ7YMlDDHRQghhBBCaKAEmcBL\/Qik57h1rsQ8KcoirX6OT58zIKXICaR\/yGdwuo\/OhB5h49TAolANlExgjhZFF544t485lXRBZQvjLBoo2TBPdAPl5MCUUmNiWkmMkMYRU5YxkjRE7K\/L32cSqRLDRVJ+ZD9RGigAoXvy\/oIFC1LG\/M2bN1axIOQOOl3kcWETNxnthqUXeDYMFD2UzO5Q6seOokT2QkdxGihhjosQQgghhNBACTKB1ruYuBWJRXkDWQYP\/XSePHmiGh5AqEUR9QQfXVfS3fejLqPMK5zSfDIdH7qByvbxwFLSQrJlYAQZPxooJp8+fTLKy8tjPUYaKC7EVfOkUA0UhNOhx7YsgwJAekidHkYHVxu5apj044KEsZYQOQg5a36MhrAGiu5il5SUGA8ePDCGhoZUvpzUGRHpbbLiNlDCHBchhBBCCCleAwVtfFEAVXT48GHrvrGxsdF6HfeW9jQYPY0cD+zskeG4d9db+ton+ZWVlRlbyIY5PoD0Dj1CRB4monisHBuiEJzmO5mOT+qeQKtWrVLtkt3U3d0d+efzMn6ZCDu+YQwOzPNQtwRKV74h6PbxoB3zc3T0sf8W0HFH1keXHzJLBkpc5oluoNT0TSjV9n9UqhswdWpwUkn+rh80dSKp+oFJU4OmTiZVJ0quV9v\/Qamme1wpagMFIBdSwqXs\/dFhlOjdeCSfEAWZ9Nf27NmjnMNsGiiI7tCP217JWV8Px6tXco7TQAlzXIQQQgghpHgNFL2Iaiah5a8dvZCsRGKgmYHc14rQTtavQRHF8eE1mBt6GpFelxH30Cgs6kSm43O7\/3YSTJSoP18UBkrY8Q1jcOChryxTX18fuYGiz\/twXqJ9sd62GEIUFUwkkkUDBRN+uHJjY2OxHmC+GCh6PQ63XEOgu5uY1NtNAYQB6o61CBc8hMu5oV\/E7W4jwHeVqRK4XkxoYmJixo9VWpTJRRc5c6j0DUNHb9eGi7XX4wLyg4ab7fdzBT0uQgghhBBSvAaKnuaSSU7zHcyFkEKycOFCx3VgpOA+1Sl64dChQ9ZySEl3IuzxAUQi4L7YbnjgmK9fv+46ZpmOT9J\/vAipMVF\/Pv34UFQ1CFGMrxMYV1nv0aNHjsvo9THdDKYw20eUkdt3hPkWmpH4iaohERko2QJhX1Bl1z9KVT3jSjU9E0rVn\/8N1fROKFX3mTrWa6q6J1U1lsaVjvWaqukZU6rs\/EdJ9jtb4IIHZxTHYDczZgtUGUfOJPIO7ceEHyHeg6LIgyyE4yKEEEIIIblpoEQFisgiJQNmCdLsUSMSEQC5dN+JeQWOC40r8JCxECbPDQ0NyhCoqKjgSe4yP4JJhAfwOC+RRhakWDChgZI3BgohhBBCCCEkHHEbKGR2gHECAwXlJAjJJjlvoKC+ClTR0aW0\/9FbpQNd70z9NaZU8fid0v7P\/1bqSuqxqQNPTVV8XgeqfDKmVPE4qc\/bVGrvUpL9EkIIIYQQQvITGiiFB1Jg0LkUdV3caiwSEhc0UGigEEIIIYQQUpDQQCk8UAMFhVGZkkJmg5w3UND+FxJDQ1JrpMhrVJLtyn5kv4QQQgghhJD8hAZK4SEtmQmZDWig0EAhhBBCCCGkIKGBQgiJkpw3UAghhBBCCCEkCDRQCCFRQgOFEEIIIYQQUpDQQCGERAkNFEIIIYQQQkhBQgOFEBIlNFAIIYQQQgghBQkNFEJIlORNEdlEIqH09OlTJXsR2L6+PqXXr18rTU1NKRFCCCGEEEKKExoohJAooYFCCCGEEEIIKUhooBBCoiTnDRQxTg5d+UvpyMPXSlWdr5RqHr1WOnwnoVTTdlvpzt17So8fP1bK1MZYjBnZH9sYE0IIIYQQkt\/QQCGERAkNFBoosfLs2TPjxo0bSpOTkxwQQgghhBCSNWigEEKiJOcNFDE2ym6MKG2+ljB11dSW66NK26+NKJXdGlXadeuFUvlNU3vuvjR1J6nk3+Wfl9X1e\/tfSmKkEBOYSW\/fvs2od+\/epaz322+\/GV988YXS6Oho0YzXx48frTH577\/\/eAIRQgghhMwCXg0U3K8NDQ0Zra2tnucAmEM9fPjQaGxsNGpra42Wlhbj1q1bxvj4eKSfAcdz8eJFo76+3nj06JG6z0zHp0+fPN23u92\/+xnb27dvG+fPnzeOHz+u\/nv\/\/n1jYmLC97aCjH+UhNk\/HhhfvnxZnQOnTp0yrl69avz999+hjyns+RXl90NooNBA8clPP\/1kGSGZpP+oi9VAKdbPTQghhBCSDwYKJszPnz83Lly4YOzdu9f47rvvrHs3TFIzTZjnz5\/vei\/8zTffqAlv2IdoqOu4cuXKGdv\/+uuv1WTYDUyavd63i\/7991\/Px4Xo\/W3bthlffvml47YwljB80n3+MOMflWESdv+4xy8rK3M9B6qrq43p6Wnfxxb2\/Iri+yF5bKBIis2iludKy84PKS1tHFRa3jSktLJlWGnNxYRSycXhVLUmlErbk2obVippSyitvTCstOySKTFuCA0UGiiEEEIIIYVloOA+3+1eNtME+smTJ9ayMDh27txplJeXG0uXLk3ZzunTpwMf94sXL4wlS5ZY28K2S0tLlXkirzU0NDiui+OP00A5efKktd7cuXONjRs3qs+\/bNmylG2mM3nCjH8UhN0\/onZ+\/PFHa51vv\/3W2Lp1q7Fw4cKUbR09etT3sYU9v6L4fkgMBsrY2JgKKRoZGaGBUmQGyqVLl4z+\/n5X6W5msRoJzc3N6mIHvXnzhicQIYQQQkgOGiiYlB44cMDYvn275wk07mmRqgGTQwfRBpjU6k\/6g4B76VWrVlnbQUqIgAeV+nsPHjyYsf7w8LBRU1OTVlVVVSmTbD+REpigl5SUqBqH+no47qamppRIiVevXqU1MIKMf5QGStD9wxiR5RHNId1fMQYwQBYsWGCNgR9zKorzK4rvh8RgoMA8wckCoUhr3AZKdfe4UvPIJ6XW0WmllqQu2NQ8YuriC1PnRaOmZL1mm6qejinJfslMA8WPscRIDEIIIYQQkmsGCia8SJERzp49G8kEHpNUPVIgSH2Rzs5Oa30YHXYw6UXEA97fsWNHoONE6orsA3U\/\/CBmgRt4gCjb\/vPPP123Ecf4+\/kMYfYvJhaigpyAASLbQ6pQVHg5v6L4fkgMBgpys8RAidNEyTcDBUV5YDBcu3bNuHnzptHb26vGN1OO2fv3742+vj6Vs9jR0aEunF4LBGHbuADA1IJD3dXVpRxLv27nbBsoKIiFbV+5ckWNW6Yfv309XABwgRKnFWOKiwqipZzWwXuQfDf4LyJosH8U1ApzfE7bJ4QQQgghuWGg2IlyAo+UCWwHNSgyFXzNNMF1m3zryyDixO+YzJs3T62LY436XvXcuXPWsaF4qReybaCE3f8PP\/xgRYE4zQkkwgfnAOYkOpiryDzB63wnyvMryPdDIjBQAEyTuE0UMTJOPp9SakpMp6hhxFRjwpQYJ41JnXORvN+UlGynbmBKya+BggsPnEaEwDnl0rm5e6iEDOdYz2fUC0Sh2na6fcI9disyhB9VVFWW4zRQYPQcOnTI+Oqrr2YUSEJFazfQFrmiomJGgSTkHiKcEaFr8po9LPH3339POS4474sXL7ZegwkV5vjs2yeEEEIIIYVvoKADjpgTiFLwC+7d5d4WNVDcwNxLjreurs7XPn7++WdrruHXfPEC7pvl2NLNZfLZQJExlBQeu0EiBotThAoeesu6mD9m8\/wK+v2QiAyUbJgo+WKg6PloMolHoR6ZdLsZKLrBgGURkmWfqN+5c2fGenAbN23aNMMwgQmgGwpRtVGLy0DB\/yR0owMXBIyb\/hnQussOokrWrFmT8vlxoZJwRrupYjdQ9OOCC2tfXgyUoMfH1CVCCCGEkOIzUA4ePGhtBzUo\/IKIE1l\/z549rsshcl2W27dvn+ft4yFj3JNnPcXE6xjkm4Fy9+7dlDnYmTNn1HwDD17R1Ucetg4MDET6HYQ9v4J+PyRCAyVuE0WMjBP9U0qnh6eV6pI6mzB1atiU\/C3LyetikMjrZxKmZDsNSdUOTin5MVAQCbJo0SLLBEF6k4AoCUzG9Rw7+4\/g119\/VfuSST7CufQfx+7du2esh9ZV+o8W7qVEm8BcwUUVqSZwKXPZQDl27Jj1PgoeyRigOLH8uFGE6cOHDynrHTlyxFoP7qtsGxctuLri+noxUOQChwJS9+7dM3p6eqxQu6DHRwOFEEIIIaSwDRSkfCOCA\/f6iApfvXq1FdmB7QUB0elyHJWVla7LIV1fltuyZYunbWNeIvev6O4SZbq\/gAm5XuTUa4pKvhkoQO92A61du9Z68IqHuijn4ARKBmBuBaHsQzbPr6DfD4nYQAHd3d2xmCj5YKDAuJATESd2FOgXRXt4FibkespPNlpQ6QbK8uXL1cXBLif3O52RAFMJxgXe27Bhw4x1Uc\/FqbgVxkaidLC+U70SmFB667dMBsr169dnbCPo8dFAIYQQQggpfAPFHg0u96aolReUtrY2T\/UpEGUuyyE62gv6A1g8NIwaPESWaHAItR3jNDBm20DBQ\/TNmzc7llKAERaWqM+vMN8PicFAAXgqr5soMFWiMlCOD0wpiSEixsfphKlTSdUnVTeUlCyX1Nmk7K\/L30EMFKBP1pES4qcVmBsS1YJoCh20EdZ7wsfhHqczUNwE19WPgaJ\/DicTCOelvP\/HH39Yr+s5n+mc+fXr13syUNxyD4MeHw0UQgghhJDCN1AQRW5Pn5foA7S5DXKPrh8HSgS4gcgBWQ71EDMxNDRkPYDEpD9q8ODx+++\/T5kPxW1gzKaBAvME7aDd5kVz5sxRc4kwRHl+hf1+SEwGCkA+mBgobmFLhWigwCHWT2yExWEM\/FRGRmQFIncQytXS0mI5hHYDRe87bp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EreJIkbGyedTSk2J6RQ1jJhqTJgS46QxqXMukvebkpLt1A1MKfk1UHByw8lDDpyTceDmviJtBc6mvVaIFDBCNeh0+0Thpfnz57sWYI2q64wYFsuXL1dOpuwDZo0TJ06cUO\/PmzdP5UQ6GSi\/\/fZbSp0TP+4sDRRCCCGEEOLXQMG9saRCoAaGG\/pEta6ubsb7P\/\/8c0qKhH2CLBNYtwgAN8IeH9aX11FA1A08sPVrcngFKfiy7XRzmdkwUHJhfGF8yHuYP6YDdWRk2cbGRlVCwYuBEtf5SSIoIhu3iZIvBgpa7+rmBQr0oJCSdJdxM1B0EwHLIg\/Q3pHGyRVGey09gkMMk8WLF6fkx0XVRks3UGDayPYrKiocjR0pygT3FQWUnAwURMvox48futfjpYFCCCGEEEL8GiiIOJD7wj179riuh8hwWQ5p53bu3r2bcg9+5swZNTFFBLikeCCNI1PNQDthj+\/ly5fW66gr4gaiv\/Vip1GC+YxsG3VScslAyYXxRV0VLwYT0oMkFQhmDeZYXg2UuM5PElEXnjhNFDEyTvRPKZ0enlaqS+pswtSpYVPytywnr4tBIq+fSZiS7TQkVTs4peTHQMHJvGjRIssEQXqTMDk5qaI09Bw\/nYMHD6ofFfYl6TAI58Lr6brQoI+3\/qOAeynRJjBX8KO\/cuWK8enTp8gNFPQdF5MHESb2sDB8Xjk2fC43AwXjtnnz5hnGExzbTNEouoGCz4nQOjfpeY40UAghhBBCitdAQfS33BfiQZ8bSIeX5dBdxQl7VxQUFJUagKihgXR+v4Q9PswnpH4Hogzc7qlx\/yzr46FsVOhNItzSR2bTQMmF8YUxgrkMhLIPTmA+h\/opWB8PyDE\/BF4NlLjOTxJhG+Pu7u5YTJR8MFD0UK50uXB+0H+09pbBqPysp\/x46VMepYEC9J7idmcZUSlSVRsXFTcDBXz48MGxvsq6detSjKh0Boof0UAhhBBCCCleAwVdUbykriAqWpZDVLkTTg8DRZioByGK49PT7VFf0Q5MAH0ZtLuNAty76218nfY92wZKvoyvzKfwoFyfV\/sxUOI4P0mEBgpAS2PdRIGpEpWBcnxgSkkMETE+TidMnUqqPqm6oaRkuaTOJmV\/Xf4OYqAAvY84Cie5FVf1g0S1wN3UuXTpUkr732y0nrIbKHBL5Rh27dplLQe3VOrASEhaOgNFftwtLS3GnDlzUn7YiHJBapSTs0sDhRBCCCGE+DVQ9Ik67jPdQOSELId6g073rzU1Na73nLivxT27X6I4Pj19BPOJnp4e672xsbGU+hhRPQBGtL3eKQbzoaCfO04DJR\/GF6Uf3OrveDVQ4jo\/ScQGCkC+lRgoUYQF5YuBAgfTPlHHGMBQ8AqiTuAwwpyAoaCHh+nofb3\/+OOPrJwodgNFN0pgdOBiAfS6Jvg8XgwUAeehXhMm3WfUDRREwOCi7SbpM08DhRBCCCGkuA2Uy5cvW\/eFx44dc10PKevp7h\/1e39ECyDFAhNWey1Dv5PUqI4P9896uv\/\/\/vc\/FRUhDywRVSHvo8tLGFBXRIqSYl+tra2hjI04DZRcH1\/Mn2SOhdQh+0N5rwZKXOcnidhAsddCQURKVAZKbf+Ukj1Fx57CI7JSdUTJlB35uz4pWV+MlaAGCly+AwcOzJj8IzLFrVMNwI8CoWRIWXFzCO0Git7VBtWYZ8NAAb\/\/\/vuM6s4opmTP9fNqoAgYL734lFObYxaRJYQQQgghfg0UvQMK7t3dSCQSrsVC9QKjiMTWo6VR3wIRB\/rkOt1cwE4Uxydgcqyn1EjtC5gVeq2SI0eOhDJPUOtEipIGfYCeLQMl18cX3VllvY0bNxq\/\/PJLirZt25ZSWwWvodZJts5PEqGBEod5kk8Gipgo6JgjxXnslY+d0EOrUNcEPxQ4hrj4SHtiu4GCH4qs09DQMGsGin4BwnH\/\/fffVgcguLtBDRQA11c3UfCZaaAQQgghhJAwBgoaDKRr1CDoEcz79+9PeQ9dWeQ9p8knIrW3bt1qLYPmEF6J4vh00NCit7fXaG9vV3MbSf1vbm621m9qago0tqjLKCUHMI\/JVJMjFwyUXB9fFHr1W6Jg\/fr1WTs\/SQQGCk4STPbjME90A6Xu+ZTS2ZFpU8M2JV+XNsVimEiKj\/wthkmDSIrLJpc7MTClFMRAsf\/o9B8AwqXQ9kpHr84MZxMFVXVQcdnJQEHHHS\/OadwGCqJn5KIplazFeZWOQEENFPvnxFjSQCGEEEIIIWEMFNxvSxoD7rXduqighofcP9ofhK5Zs8a6v8cE2gm9KyUeCnoliuPzgh7pkOle2u3+WtJ2cLx+CsbOpoGS6+OLB+loM+wmPWthw4YN6jV0Z83W+UlCGihxmyf5bKCIwYAT261bjd7JxslYcDNQ9JCwBQsWpJgV2TRQQFVV1QwX1N7rPKiBorcfthdfooFCCCGEEEL8Gij2e1OkNDiB9AiJJLffv0p9C7znVu8Q86R0RWjTEfb4MoHIEYkaD9rCWI9giKIjaLYMlHwZXzf0Rh5uET9xn580UAIaKNkwT3QD5eTAlFJjYlpJjJDGEVOWMZI0ROyvy99nEqkSw0VSfmQ\/fg0Ut647el0URJzo6BEqdhPgzZs3VgEhFCPSt48fgt7159ChQ67uadwGip5jJ3rw4IEnA2VoaCjtccNN1T8jDRRCCCGEEBLWQEHXlHTR3GjHK1EKTmkeepcVtyKcKG8gy+Chqc6TJ09UwwjI\/oA1iuNLx6dPn1LqKTpNwjMdn940Ag98o+gI6sdAyXR8mZjt8Y3bQAl7fpKYDJS4ap7ko4Ei+X8IuUIhJfxw0PoKoWwopiQOICbybsYC6qbAeICpgDw5qX8i0ttjAZhX+vsoEITjRc9yVG\/Gjws\/BmwvTgMFrFixIuUiajeT3AwU9ERHyBhSdZCPiHETcw55g3LhwtjZPz8NFEIIIYQQEsRAAXqUOCIo5P4VxUOlHS\/uQZ3mA3oaPh544r7bPk\/SW\/raJ\/mS9p6uxW2Y40PHFczPpCOmPhboCCPbRZcXJzIdn57Cv2rVKtUO2E3d3d2O+8BcCfMm0eHDh1OaZMjrTg9cvYxfJmZzfFEzBXVLIL1uZFQGStjzk2TBQInLPNENlJq+CaXa\/o9KdQOmTg1OKsnf9YOmTiRVPzBpatDUyaTqRMn1avs\/KNV0jyv5MVDw+XUzAxN\/MU5EMFfsYOIv4V12oY6IdLSRwkz2Cs7Yptv6IvxA4zZQ4GyiIjTkFMKXzkCxjxsia\/BZ9dedOg3RQCGEEEIIIUENFDQswORfT2PQI7xxj43CoG7ohTrlISKaKtg7SaLdrV+DIuzxDQ4OphwX2uvqbXUhRCnAxAhioGSaf+iCieKEXqQ1kzAWURsoszm+eGguy9XX10duoIQ9P0lMBgqiBOAKjo2NxXqA+WCgwIXESYofob23Ntw9GAxuqSr4EUjrL\/mxIldOIjL0NlPYvp379++nRIDo7ZPhgPrN2XND6rUEMSD0dsd6FA6MFUlTchKicuzti+3H4xSdYwfnd7oxJIQQQgghxWWgyD087rvthsDChQuN69evZ5wLIYUEyzrdx2Kiivt8pzkAUtPdGiVEcXyIorDPSUSIHkH0d7o0+kzH57ZtJyE1xwk9jSaT7PNN\/fhQVDUoszW+ep1HN4PJDRyXrOs2Twp7fpKYDJRsgbAvqLLrH6WqnnGlmp4JperP\/4ZqeieUqvtMHes1Vd2TqhpL40rHek3V9IwpVXb+oyT79Yu0skKEDsK63Gqj2NdBrh3y+ewFYXFS4z0oXX4hUnfg5OKY379\/nzcnIMYHLi7SnVpbW5XZBFMIUT38QRNCCCGEkLgMFH0ijfT4jo4O9RDTzz0oahPi3h+TUaSgYzu4t42iLkiY48P8AiYF6pXguJCmEdWD1VygoaFBGQEVFRUc31k+P2mg0EAJZaAQQgghhBBC8sdAIfkHjBMYKCgnQUg2yXkDBZEIUEVHl9L+R2+VDnS9M\/XXmFLF43dK+z\/\/W6krqcemDjw1VfF5HajyyZhSxeOkPm9Tqb1LSfZbCOD7lUJFfpTL5wUhhBBCCCGZoIFSeCAFBvUmUbcEzTwIySY0UIrAQMGFxWuOoS5ekAghhBBCSD5DA6XwQA0UFG4tpJQkkj\/kvIHy4cMHJTE0JLVGirxGJdmu7Ef2Wwgg9w3trPwK6xFCCCGEEJKv0EApPLzUmCQkLmigFIGBQgghhBBCSDFCA4UQEiU5b6AQQgghhBBCSBBooBBCooQGCiGEEEIIIaQgoYFCCIkSGiiEEEIIIYSQgoQGCiEkSmigEEIIIYQQQgoSGiiEkCjJmyKyiURC6enTp0r2IrB9fX1Kr1+\/VpqamlIihBBCCCGEFCc0UAghUUIDhRBCCCGEEFKQ0EAhhERJzhsoYpwcuvKX0pGHr5WqOl8p1Tx6rXT4TkKppu220p2795QeP36slKmNsRgzsj+2MSaEEEIIISS\/oYFCCIkSGig0UAghhBBCCClIaKAQQqIk5w0UMTbKbowobb6WMHXV1Jbro0rbr40old0aVdp164VS+U1Te+6+NHUnqeTf5Z+X1fV7+19KYqQQE5hJb9++naHx8XEOTgg+fvxojeV\/\/\/3HASGEEEIIiZB0Bgpev337tnH+\/Hnj+PHj6r\/37983JiYmfO0Dc4aLFy8a9fX1xqNHj9T9XdTgPnFoaMhobW0NNEcJu36+j1+c4\/Pvv\/8a\/f39Rltbm3H06FHj7Nmzxp9\/\/mmMjo7mxPjG9f3TQKGBQgMlDT\/99JPxxRdfOGrevHlGaWmpcfXqVWN6epqD5YPffvvNGsewF1lCCCGEEJLZQEH0+bZt24wvv\/zS8d72u+++UxP6TA+3UHdx5cqVM9b\/+uuv1WQ37IT++fPnxoULF4y9e\/eqY5Lt37p1K\/b105EP45et8WlqajK++eYb13lSWVnZrIxvnN8\/DZQcN1AkxWZRy3OlZeeHlJY2DiotbxpSWtkyrLTmYkKp5OJwqloTSqXtSbUNK5W0JZTWXhhWWnbJlBg3JLOBomv37t0cLBoohBBCCCE5a6CcPHnSuv+aO3eusXHjRqO8vNxYtmxZyn1tukn8ixcvjCVLlljLLl26VD1QxORfXmtoaAh83JiHuN1ve5kAh10\/HfkwfnGPD6JODhw4YK0Ds2Pt2rXKrMDn+Pbbb9XrW7ZsmZXxjfP7p4ES0EAZGxszHj58aIyMjNBAKTIDpaOjw3j27Jnx4MED9cNdvXp1yo8S7xNvNDc3Gzt37lR68+YNB4QQQgghJAsGSklJiXHjxo2U6Gk8tUdEgdzTIrLg1atXM7aJ5VatWmUthyhsAent+nu4Xw4zwYexgIn69u3bAxkoQdfPNMHP9fGLe3xqamqs5detW6fS8XUmJyeNO3fuqEiRbI9v3N8\/DZSABgrME5wQEIq0xm2gVHePKzWPfFJqHZ1Waknqgk3NI6YuvjB1XjRqStZrtqnq6ZiS7JfMNFDQLlrn06dPKT\/KX3\/9lQNGCCGEEEJy0kCZmppKuw4ebMl9LWpZ2Ons7LTer6qqmvE+JrUSgbBjx45Ax41jRIqLgLoafibAYdfPtO1cH784xwcmz5w5c9SyK1asCFSTJM7xjfv7p4ES0EBBBIIYKHGaKPlmoOAHBMfv2rVrxs2bN43e3l41vplyAN+\/f6+MCRQKQgQHLixeC7Ri2\/iBwNSCg9vV1aXC4hBalg0DBeC45X2Er6UDTirSVbAOhH9nGh8Uk3r37p2Sviz+hwh3dnBw0PXigbHE9+G2TJjvwe24sA62ce\/evbSV393Wj\/L8IIQQQgihgeKdc+fOWfe1KN6ZbgKLOhOZJrnDw8OhP0fYCXA2J9C5OH5Rjs\/p06etZRFN7hfMheT+P5NZEmR8Z\/v7p4GSBpgmcZsoYmScfD6l1JSYTlHDiKnGhCkxThqTOucieb8pKdlO3cCUkl8DBZPfU6dOqTw1p1wzN3cQlZThrOr5fnoBJVSjTrdPFAaaP3++4z6RixeVI5rJQEGImryPokduFwv8eMVR1oVxQ0iam4nw+++\/p9QKefLkyYzUIYSowTwCCJv75ZdfZuwLF2OYEVF9D\/bjgkmDvEe96NNXX32lLrRe1o\/y\/CCEEEIIoYHi30A5dOiQdb9lv9fCvbXc56GGhxuYG8k26urqispAycXxi3J8fvjhB+se35664wU89JZ9Yf4Y5fjSQMlxAyUbJkq+GCi6EwktXLhQFfrBDyudgaIXEcWyMAFkHRHMCTuIXNi0adMMw2Tx4sUpk\/eoohQyGSh6HuCRI0c8He+iRYuU9Ne2bt3q6MTq44QIDDejChc0uLmoeO1WOAnHGtX3oK93\/fp1Y8GCBa77vXv3btr1nQyUoMdFCCGEEEKCGSi435L7LEQ66yBiQt7bs2eP6zZwvyzL7du3r6gMlFwcv6jGBw9pZTnM9QQ8oEWEOGq2ZJpbYxm\/BojX8aWBkgcGStwmihgZJ\/qnlE4PTyvVJXU2YerUsCn5W5aT18UgkdfPJEzJdhqSqh2cUvJjoCBqQowATG6R3qT\/yBAVoeeg6Rw8eFDVDMG+pEgQDAC8nq6zTW1tbYpxAvdSok1gVuCic+XKFVWfJG4DBS6qTOrxX6cwuxMnTljrI3JEbxGN5fViUU7RGrqRIKqsrLSK\/SJtSI9mwX\/RFg3nI\/alV7OGyRTV9+B0XKi2ffnyZbUtVMyW1zGGfg2UoMdFCCGEEEL8GyiYkOrtYu0P9hAdrN+LuoF0ev3esFgMlFwdv6jGB\/frshy647x8+dLYsGHDjJbDmO8MDAw4bqO\/v1\/NCyCUGYhyfGmg5ImBArq7u2MxUfLBQIFxof9YokC\/aMBcsP9w9ZSOuPuk2w0UfL+IemhpaUkpHosLR2Nj44x1UQhK+qMjpQYdnOzAFJB0G\/zXfk7ajYrW1taU9\/G3\/j4MFT36BubDvHnzrPdhMoX9HpyOy\/4\/Aj1MEW6xXwMl6HERQgghhBB\/Bgoegurp36g9Z6etrc1T\/QnchzpFKhSygZLL4xfV+ODBuB5Nj6wD\/BslFTAP1D8\/HizjQXM2x5cGSh4ZKAAtjXUTBaZKVAbK8YEpJTFExPg4nTB1Kqn6pOqGkpLlkjqblP11+TuIgQL0PuYo7KO3nAqKRLUgLUXn0qVLKXU\/oiwW68VAcdLy5ctdv29EY8hyyNlzA+aDUzszu9HgZBjhgqIbCk6pS3Cvg6Q2uX0P9uP6+eefHb93WR8ucVQGSqbjIoQQQggh3g0URIt\/\/\/33KffzmSaibjXuACIDZDlMrgvdQMn18YtqfBDhb6\/jiLmZ1HHEvEyvUYI5otcHt1GMLw2UPDNQAOo8iIGCLjTFYqDAQdV\/TJI+4ucHg6gCRO4glAvRHeIw2ifIR48etfbzxx9\/ZOVEyWSgpMvfO3bsmLUczBQ3dEcaKTd+jAYUb5X30evcCT1aJp2B4vV78GqASJ5iWAPFz3ERQgghhBBvBgrqckhhUEQO2yOddfQHg7jHdQPFRfV5QSEbKPkwflGND+7D9TmQWwoO0ntkmfb29qyNLw2UPDNQ7LVQEJESlYFS2z+lZE\/RsafwiKxUHVEyZUf+rk9K1hdjJaiBAtcRE3e7sQDXUTrDOIGIBRgH69atczUn7BNkva6GU8pM3AYKvucPHz6kdN6x137RQY0OWS5dGBta\/spy6KCTTQMlyPeQDQMl6HERQgghhJDMBgomp7hHwz0VUs4zPQDWO6i43XMC1OCT5XAPWqgGSr6MX1Tjg\/mhLIeIEDf0WiVODSziGl8aKHlkoMRhnuSTgSImCkyFkpKSGd1xzpw547iO3r0GdU3gViKaBT8OaU9snyDDXJB1Ghoasm6g6EVkddNoxYoVjhE3e\/futZZB4Sg3dEMGxkI2DZQg30M2DJSgx0UIIYQQQtIbKLj3kpRo3Gd5qVeB5gdeCvmjyYEst3\/\/\/oI0UPJp\/KIaHxSNleVKS0tdl9PnJrt27cra+NJAyQMDBXlemMzFYZ7oBkrd8ymlsyPTpoZtSr4ubYrFMJEUH\/lbDJMGkRSXTS53YmBKKYiBYv\/R651hEKGBH5yOnkOHyT2iOnTQLcZpgoyOO16c22wYKGjZpdd\/cToevWNQuhQevRCsvSd6nAZK0O8hbgMlzHERQgghhBB3AwU1JSQtAvfpXgty4n5Muk\/iXkxqX9hBjQq5j3N7kJrPBkq+jV9U44O5b7rOnoLerjldDciox5cGSo4bKHGbJ\/lsoACkX6CtlVuf7rKyMus9dKqx4zZB1kPCFixYYLUvng0DBaDnuR5xY\/9RdnR0eHKQ0a7XLZ8wTgMl6PcQt4ES5rgIIYQQQoi7gbJ169bAHS31e1a0pHVi06ZNViS6031cvhso+TZ+UY7Ptm3bMt7\/6+2aUbswm+NLAyVHDZRsmCe6gXJyYEqpMTGtJEZI44gpyxhJGiL21+XvM4lUieEiKT+yH78GilvXHT3FBREFOnqECiJWdN68eaOqOuO9OXPmpGwfaTJ61AdcTTf3NhsGCjh48GCKqaOfU\/pnQe6e0\/\/EcGEUNxrthtHWOFsGStDvIW4DJcxxEUIIIYQQZwMF3R71KAK\/HS17enrS3neiLqDc1zqlqTx58kRNqiH7A9ZcMFAyHV8+jl+U46PXbayoqHBcRn8Q6mYSuRF2fGmg5KiBElfNk3w0UCQ\/DbUpEK716dMn1XoLoVYwDMQ9RSiWju6+om7KgwcPjKGhIaOpqcmqbyHChUYH5pX+PnLrcLwwBtCtBREc+OFie9kwUBCO9+OPP1rLwJnVTR09DA+93Ht7e9WkHxcEnEsSogY1Nzf7MhrCGihhvoc4DZQwx0UIIYQQQpwNFKkrAa1atUq103VTd3e343b1KHNECMjDLBQ\/lXazuP93mk9UVlZa665evdpx+5hLYF4hOnz4cEoTCXkd94ZOD1LDrJ\/p+PJh\/DIRZnzwN2o\/6t2E5PjxX3QTTRd9j3nQ+vXrlZzKG0QxvmHPHxKzgRKXeaIbKDV9E0q1\/R+V6gZMnRqcVJK\/6wdNnUiqfmDS1KCpk0nViZLr1fZ\/UKrpHlfyY6Dg8+uTWTimYpyIYK7YQVQBLgxOnVXQolaf8KNw0JEjR1LWxzbd1hfhB5oNAwWg25B+PHp\/chglejceGSd8Lv21PXv2KAMqmwZKmO8hTgMl7PlBCCGEEEIDZaaBkun+WRcmqU6gzS4mt7IcHm7pEeLYh1v7Wi8GgF5ENZNwLFGun+n48mH8MhF2fFEMV38AjIhwGCKIxJfXli9fPiOqHuChqCxTX18fy\/iG\/XwkBgMFE2K4VmNjY7EeYD4YKPhh7Nu3T10EJNxMb2916dIlV2cPkSLSmkouFsj5w48SRgIuCroDaQf5dboDqrdP3rlzZ2Q5g17D0HR3E5N6uymAsRBX2X68CFdzA+MryyLCxo5eqAnpRE7s2LHDWsZeNybo95DpuAAibvA+3GS\/nyvs+UEIIYQQQgMl1UCx36+nE1If0s0BcF9mn\/AuXLjQuH79uut6SL+XZZGy7YSe5pJJTvOxMOtnOr5cGr9169YFOi\/Cji\/AHNrefVV\/oOvUnRTo9SOdDJAoxjeKz0ciNlCyBcKSoMquf5SqesaVanomlKo\/\/xuq6Z1Qqu4zdazXVHVPqmosjSsd6zVV0zOmVNn5j5Ls1y+Tk5Mq6gMROpgQe6lNgXWQ64d8PvvEHsYL3oPS5b8hogJOI44ZnXFyHVww5XizUQTX63cX9nsopuMihBBCCMlHAyWO+1qk16NxAh5yMSUi\/vFraGhIW4Mk28ePuiioyQJzxCnqhNBAoYFCCCGEEEIIyWmyYaCQ7APjBAYKykkQkk1y3kBBfRGooqNLaf+jt0oHut6Z+mtMqeLxO6X9n\/+t1JXUY1MHnpqq+LwOVPlkTKnicVKft6nU3qUk+y0E8P1KoSI\/yuXzghBCCCGEkEzQQCk8EOWBepOom+JWg5CQuKCBUgQGCi4sXnPgdPGCRAghhBBC8hkaKIUHaqCUlpZGVuuRED\/kvIGC9riQGBqSWiNFXqOSbFf2I\/stBFDA6MqVK77lVviIEEIIIYSQfIAGSuHhpcYkIXFBA6UIDBRCCCGEEEKKERoohJAoyXkDhRBCCCGEEEKCQAOFEBIlNFAIIYQQQgghBQkNFEJIlNBAIYQQQgghhBQkNFAIIVFCA4UQQgghhBBSkNBAIYRESd4UkU0kEkpPnz5VsheB7evrU3r9+rXS1NSUEiGEEEIIIaQ4oYFCCIkSGiiEEEIIIYSQgoQGCiEkSnLeQBHj5NCVv5SOPHytVNX5Sqnm0Wulw3cSSjVtt5Xu3L2n9PjxY6VMbYzFmJH9sY0xIYQQQggh+Q0NFEJIlNBAoYGSFzx79sy4ceOG0uTkJAeEEEIIIYRkhAYKISRKct5AEWOj7MaI0uZrCVNXTW25Pqq0\/dqIUtmtUaVdt14old80tefuS1N3kkr+Xf55WV2\/t\/+lJEZKsTM+Pm68ffs2sP79999IjuO3334zvvjiC6XR0VH+cgkhhBBCSEa8Gij\/\/fefMTQ0ZLS2tnqeA2C7t2\/fNs6fP28cP35c\/ff+\/fvGxMREpJ8Bx3Px4kWjvr7eePTokfHx48e0y3\/69MnX\/fq7d+8Cj21Unz\/I+EdJ2P3HcfxxnF8ocSHf+\/T0NC8QNFBooETNwoULLeMiiK5evUoDhRBCCCGE5JSBggnv8+fPjQsXLhh79+41vvvuO+te89atW2m3iej1bdu2GV9++aXj\/S+2BcMD+wgD6jquXLlyxva\/\/vprNZl2A5Nuv\/fsfh56RvH5w4x\/VIZJmP3HefxxnV8wTDZu3Ght5+HDh7xAFKKBIik2i1qeKy07P6S0tHFQaXnTkNLKlmGlNRcTSiUXh1PVmlAqbU+qbVippC2htPbCsNKyS6bEuCl29ItBEF25coUGCiGEEEIIySkDBff5bvevmSbAJ0+etJadO3eumpSWl5cby5YtS9lOOpMjEy9evDCWLFlibWvp0qVGaWmpMk\/ktYaGBsd1cfxxGihRfP4w4x8FYfcf5\/HHdX7p26WBMgsGytjYmBr0kZERGigFDELR+vv7Zwiup\/z48KN2WgaKKoSRBgohhBBCCInaQIExceDAAWP79u2+DJSSkhJVm09Pg0BEQFNTk7Wdb775xnj16pXvY8Z2Vq1a5RjRjfR6\/b0HDx7MWH94eNioqalJq6qqqpRJup90jig+f5jxj9JACbr\/OI8\/jvMLNUHtES00ULJsoGDAMYmG8IXEbaBUd48rNY98UmodnVZqSeqCTc0jpi6+MHVeNGpK1mu2qerpmJLslzjz5MkT68e3c+fO2PdHA4UQQgghhERloKAWBFJkhLNnz3qeAGPddODeWLb1559\/+j7mzs5Oa30YHXYwaf7222\/V+zt27Ag0Lkg9kX2gbocfovj8YcY\/CsLuP87jj\/r8Qo2b77\/\/fkakDA2ULBso6IoiBkqcJkq+GSiIuIAjee3aNePmzZtGb2+vGt9MOWrv3783+vr6VM5iR0eHunDCYfYCto0fMH4EcKi7urpU2F9UBVyjNlDgpMIEwWeF8O9M4+NmoOB\/IBhrjHmmglphx5kQQgghhBSGgWInygnwuXPnrG2h+Kdf9Aky6mxkWgYRJ37HZN68eVYkedhaLVF8\/mwbKFHv38\/6mAvB1IAymSVhxxffrZwrX331VUoWAQ2ULBsoAKZJ3CaKGBknn08pNSWmU9QwYqoxYUqMk8akzrlI3m9KSrZTNzCl5NdAwcl56tQpFQLnlAvn5g6ikjKcYz2fUS8QhWrb6fYJ93j+\/PmO+0SYVtRVwMMYKLhY4OIijrkujBtC0twu4HYDBaaJPQ8Qnxchb07bCDPOhBBCCCGEBopXDh06ZG3L7z0m7t0l1QI1UNzQJ8J1dXW+9vHzzz9b98B+zZe4Pn8xGSgwLmRZzB\/jHF\/USZFlGxsbjYGBARoos2mgZMNEyRcD5fTp0ymTcnSvwQQfTl86A0U3BrAs8uhkHdGdO3dmrIdoi02bNs0wEBYvXpyS3xZXdIVfA8XpeBctWqSkv7Z161ZHJ1Yfp5aWlhljpAvLRjXOhBBCCCGEBoofcJ8p20IdCz8g4kTW3bNnj+tyiKiW5fbt2+d5+6iZEtTcifPzF5OBEvY78Dq+qEeJOilYrqysTD1kpoGSAwZK3CaKGBkn+qeUTg9PK9UldTZh6tSwKflblpPXxSCR188kTMl2GpKqHZxS8mOg4GQUIwCTcqQ3CZOTkyqlRs+R0zl48KDx66+\/qn1JkSCEc+F1Obl37949Y73a2toU4wTupUSbwKzARRUdcNAHPhcMlBMnTljLr169OqVFNJxvvRgWzKh0Bog45keOHFGRJUjfwb\/19\/H5oxhnQgghhBBCA8UrmNDq7Wb9pmjg3lbWr6ysdF0O6fqy3JYtWzxtG\/MSmXyjPXIc6f5BP38xGSgwNn766SclRNXHMb6YD65YsUIthwfsmPcAGig5YqCA7u7uWEyUfDBQYFzo5kAU6BdFmAs6SGHRU1HCtEjLhoGCOiXifiJ9Bx2c7OBHLak9+K\/9nNQNFFwsnL4b\/E9GlkE\/9rDjTAghhBBCaKB4BQ9R9VR11NzzS1tbm6f6Fogyl+UQ9e4F\/QHsvXv3Ih\/rMJ+\/mAyUbIxvRUWF9aBdn5fTQMkhAwWgpbFuosBUicpAOT4wpSSGiBgfpxOmTiVVn1TdUFKyXFJnk7K\/Ln8HMVCA3qcdhX38tAJzQ6Jafvjhh5TXL126lNITPs5isVEYKJcvX7aWRc6eG7oBordrsxsoPT09jusPDg76\/h9JunEmhBBCCCE0ULyAaHO90wnmA2En8k5R2QIiD2Q51EPMxNDQkJW+vnnz5sjHOeznp4ES3fiidIRbfRwaKDlmoIC7d+9aBgq60BSLgQKHWE8hQVgcxsBLZxgB0RBwCBHKhTof4jDaJ\/ZHjx619vPHH3\/Myonjx0A5duyYtSzMFDd0xx3FYN0MFLc2xkilkv8x4L9uJpbXcSaEEEIIITRQMoG6JbiPlCf+ftsC6+gPHnEP7cbbt29T5h3pwD2yXosQXUKjJIrPTwMlmvHFPEeamiC1yz4fooGSYwaKvRYKIlKiMlBq+6eU7Ck69hQekZWqI0qm7Mjf9UnJ+mKsBDVQcGE6cODAjIKmiExBDRQ3cFLDOFi3bp1rUVT7xL68vDylmnKuGyioLeLlh4pQQlnul19+8W2gAL0oLf7HEmacCSGEEEIIDZRMk1ukl2M9pKyHfYCsd2jB3MIN1BOU5bZv3552m3pEAjrwRD25j+Lz00CJZnzRdVSOAy2qMafStW3bNut9mGp4zf7gmmTJQInDPMknA0VMFHRyKSkpmdEd58yZM47r1NTUpBRGxYmOaBb8OKQ9sX1ijxNd1mloaMh5AwX1SGRZFMZyA2Pn1knHq4EiPe3tEShBxpkQQgghhNBAcQP3pPLwDveXUTzRR3MFLw0O0ERBltu\/f7\/rch8+fFBFRGVZTMijIsrPTwMlmvFdu3at68NiN61fv54XimwaKKi\/gUloHOaJbqDUPZ9SOjsybWrYpuTr0qZYDBNJ8ZG\/xTBpEElx2eRyJwamlIIYKPaLmn4CY0L\/8uXLlGXQJUd3jnGB05GLnX1ij447XpzpXDFQ9IJV6VJ4EI7m1hPdi4Gi54LqNVCCjjMhhBBCCKGB4gRqUkhaBe7zgxSMdTM8JCUd96h4QOsEamDI8bo9qAWoo+K3W48Xov78NFCiGV88IMbDazfp0fgbNmxQr2GuRrJkoMRtnuSzgQIQAYET061PN\/pxy3voVGPHbWKvt69asGCB1b44Vw2Ujo4OTw452gzLcvaWXl4MFKRKyTIIXws7zoQQQgghhAaKE1u3bo2tI6Z+T4yWt05ITRNEujvd34oZg7lCmK5A2fr8NFCyc35hjsUaKLNkoGTDPNENlJMDU0qNiWklMUIaR0xZxkjSELG\/Ln+fSaRKDBdJ+ZH9+DVQ3AqW6nVREAmho0eoIGJF582bN1YBoDlz5qRsH4Vp9a4\/6Gzj5k7ngoGifxbk7jn9TwwXfnHbkYYjvcqdDBR0IXJCTxXS8wODjjMhhBBCCKGBYgfdImVZPIjz2xET99FoZADZH7ACdJxMF22OdrZy35wuzUefLGPu4PU+N9Pxhf38Ycc\/0\/Hlu4ESx\/jSQMkBAyWumif5aKBIfhpCppBX+OnTJ5VOApcXhoG4wwjF0tHdZdRNefDggWox1tTUZNXlcGvdC4NAf3\/Xrl3qeNETHtWX8eNA5AW2N9sGCtDDDJFeg+rfuIjjgoBzSULUoObm5hnr6waKFJnFWMM4wmeurq623lu1alWKoRRmnAkhhBBCSOEZKLhXx72k6PDhwylNGuR13DPaH1TqTQtw34k0GTd1d3fP2HdlZaW1\/urVqx2PW49iRwSCmB8oHivtbDG\/SDdfqaioCNS5M9Pxhf38Ycffy\/hlIsz+w66PeRDqjkBO5Q2iGF8aKDluoMRlnugGSk3fhFJt\/0elugFTpwYnleTv+kFTJ5KqH5g0NWjqZFJ1ouR6tf0flGq6x5X8GCj4\/PokHI6wGCcimCt2EA2BC59TMR+01kW9Dr3w6ZEjR1LWxzbd1o+rTVlQAwVGid6NR8YJn0t\/bc+ePcqASmegSCVqGSd9fVxw+vr6Ih1nQgghhBBSWAaKXoQ1k\/TOjiDT\/bcuTHKDGCjYJybPshwe+ukR6DiG9vZ218+NSbtePNZP+k6m4wv7+cOOfxQGSpj9h10fD3Plvfr6+ljGlwZKDhoomBDDVRsbG4v1APPBQEG6yb59+9RFTsLpRHCIkXLilmKDk1g3BPCDQU4jKnDDSMBFQXcg7aCrzYoVKxzbJ8PYcMuJjOJ7kX2hrbJXMBbimtuPF+FqboiBgnQbRJwgXUcfa\/wb4+b2ecOOMyGEEEIIKRwDRU+TyST7fMd+v59OSO2wg\/R7eR+p5unmGLhftU+oFy5caFy\/fj3t50aEvH7f62fOlun4wn7+sOOvHx+KogYhzP7Drt\/Z2ZnWAIlifN3AeSPrPnr0iBeIbBoo2QJhSVBl1z9KVT3jSjU9E0rVn\/8N1fROKFX3mTrWa6q6J1U1lsaVjvWaqukZU6rs\/EdJ9uuXyclJFfWBCB2k0njJNcQ6yGVEVIe9ICyMF7wHpct\/g6kAJxTH\/P79+5w\/8fA\/BDleL0VwsTz+R6AbJAidw1jjAoa6MNkYZ0IIIYQQkv8GSj6B+2Ck76MxAx7+Zbv2YS7S0NCgTACkKRFCAyWPDRRCCCGEEEJIblAIBgqZidR3QTkJQrJJzhsoqC8CVXR0Ke1\/9FbpQNc7U3+NKVU8fqe0\/\/O\/lbqSemzqwFNTFZ\/XgSqfjClVPE7q8zaV2ruUZL+FAL5fKVTkR7l8XhBCCCGEEJIJGiiFB1JgUG8SdWEQoU5INqGBUgQGip4D6Ue8IBFCCCGEkHyGBkrhgRoopaWlsdV6JCQdOW+gfPjwQUkMDUmtkSKvUUm2K\/uR\/RYCqA9y5coV3\/JSV4QQQgghhJBchQZK4eGlxiQhcUEDpQgMFEIIIYQQQooRGiiEkCjJeQOFEEIIIYQQQoJAA4UQEiU0UAghhBBCCCEFCQ0UQkiU0EAhhBBCCCGEFCQ0UAghUUIDhRBCCCGEEFKQ0EAhhERJ3hSRTSQSSk+fPlWyF4Ht6+tTev36tdLU1JQSIYQQQgghpDihgUIIiRIaKIQQQgghhJCChAYKISRKct5AEePk0JW\/lI48fK1U1flKqebRa6XDdxJKNW23le7cvaf0+PFjpUxtjMWYkf2xjTEhhBBCCCH5DQ0UQkiU0EChgUIIIYQQQkhBQgOFEBIlOW+giLFRdmNEafO1hKmrprZcH1Xafm1EqezWqNKuWy+Uym+a2nP3pak7SSX\/Lv+8rK7f2\/9SEiOFEEIIIYQQkp+kM1Dw+u3bt43z588bx48fV\/+9f\/++MTExEXh\/KCHw9u1bpenp6cg+x3\/\/\/WcMDQ0Zra2tvucoWP7ixYtGfX298ejRI+Pjx4+RjW1U4xfm8832+Mb1\/WOO\/vDhQ6OxsdGora01WlpajFu3bhnj4+O+tvPvv\/8a\/f39Rltbm3H06FHj7Nmzxp9\/\/mmMjo7yAkEDhQYKIYQQQgghxN1AQfT5tm3bjC+\/\/NL44osvZui7775ThgMm1X7AhHnjxo3WdjD5DTOhf\/78uXHhwgVj79696phku5hEewF1IVeuXDnj83399dfK7AhKFOMXxecLa5hEvf+ovv9nz54Z8+fPdxxb6JtvvlGGipfzs6mpSS3vtq2ysjJeJArNQJEUm0Utz5WWnR9SWto4qLS8aUhpZcuw0pqLCaWSi8Opak0olbYn1TasVNKWUFp7YVhp2SVTYtwQQgghhBBCCsdAOXnypDWBnDt3rpr0lpeXG8uWLUuZXPo1GfTthjVQMA9xm\/R6meC\/ePHCWLJkibXO0qVLjdLSUmWeyGsNDQ2Bji2K8Qv7+cISx\/6j+v6fPHlibQMG2M6dO9X44jvUt3\/69GnXbSDq5MCBA9ayMLvWrl2rzCKcB99++616fcuWLbxI0ECZHQPl5s2bxu7du9UJOTg4yDOLEEIIIYSQHDVQSkpKjBs3bqSkWeCJPp7Y60\/6X7165Wk\/qLloj8iIwkDBpBkT4e3bt3ue4ONzrFq1ylr+6tWr1ntI\/9Dfe\/DgQSCjIOz4hfl8URooUe0\/yu8fqTWnTp1SJpgOxhqmiR7p40ZNTY213Lp161RKkc7k5KRx584dFSlECtRAqe4eV2oe+aTUOjqt1JLUBZuaR0xdfGHqvGjUlKzXbFPV0zEl2a9Xjh07Zp2kcA0JIYQQQgghuWegoE5FOvDEX+7rUSsiE+\/evTO+\/\/77GZEMYQwUHCNScATUrfA6we\/s7LSWraqqmvE+TA2JQNixY0egYws7fmE+XxREuf84vn83YFLpkSjYtx2YZHPmzFHvr1ixIlRNH0IDJXIDBU4gTtxDhw5ZJzKKKeE1KN0JiwJOcD+vXLli9Pb2pr0YYVnZpp7vhv8hwP11i3rBNnERvXbtWtrIGLft4wKLdXGcURWcIoQQQgghZLYMlEycO3fOuq9HcdRME1oxDL766iv1RD+OCbSfCb5uYKDOR6ZlhoeHIx1zP+M3WwZKVPsP8v3L\/BHKZEY5IXVWEPHiND\/To1Sam5t5EShWA+Xk8ymlpsR0ihpGTDUmTIlx0pjUORfJ+01JyXbqBqaUvBooXV1drvlz4vrZQU4aDBf8yOwFgS5fvuy4n99\/\/91aDmFdiHJZvXp1yvpwI3E8AGFZv\/zyi+Uui\/ADf\/\/+fcbtwzSx5zHiR4qQPb8FtQghhBBCCMkXA0V\/MIrONelAnQ9ZFt1SBgYGZtVAwcNbSSVBDRQ39Il+XV1dpGPuZ\/zy3UAJ8v3jdVkGqTp++PTpkzFv3jy1LlKxnPjhhx8sQ8eeukNooMy6gYJWYH4MFFzUkDMo7+MHAKNCz5nDj8\/Ob7\/9Zr3f0dGhCjY57Q8\/GLiZqKjsdkzIiUu3fbTIsps7urAsIYQQQgghhWig6CkSiPR2A61hpcMJ7r3xkHG2DRREnMhye\/bscV2ur6\/PWm7fvn2RjrnX8ct3AyXo94+6M34NJuHgwYNpxxYP0eV9zDEFPEBHVgL2nctzfxooERooJ\/qnlE4PTyvVJXU2YerUsCn5W5aT18UgkdfPJEzJdhqSqh2cUvKTwgNnT3da8aOT\/t\/2FB69VgqiOaTw0sjIiHWxWbBggfHhwwdXg0NUWVlpFbtFVWW9GrZUbYa7jHbMelXoxYsXpzVQpL3ZkSNHVC93bB\/\/1t\/HRZcQQgghhJBCMlAwKdWLdLqlWCB1Ag9K5d5aalHMtoGCe3d9ruAGCpTKclF2YvE6fvluoIT5\/mG8\/PTTT0qI+ncDc0mkVyHDAO2WJfsA8zQcrxPIJJD9I9Xn5cuXxoYNG2YUuMW2cKyEBkrWDRQnY8StiCwKFYlLiRPZDipkyzZaW1vTGhz29\/G3\/j4MFRQQ0o0eCfeC7Ply+vZxsXP67LgIyzJogUUIIYQQQkihGCjPnj1LSX9HXUM3KioqrBR3dGARZttAaWtr81R\/BPMEp0iFMPgZv3w3ULLx\/W\/atGnGA3TMJVE70w29vMSiRYuMhQsXqn\/Pnz9fmSb694OMgzgK3dJAyRED5fjAlJIYImJ8nE6YOpVUfVJ1Q0nJckmdTcr+uvwdp4Fy6dKltH3R8T3I+3\/88YerweG0Li5Y8j7y4XTzRIC7LMvY39e339PT43j8KEIb9YWWEEIIIYSQ2TZQ8KBT76SCQqhuoLOMW\/2Q2TZQ9OVQTNQNRIbIcphch8XP+OW7gZKt7\/\/XX39V0S326BGYIEePHlV1Ne2gOYm+LDITMAeVGpZSi1PeR50cNgqhgZKzBgpOdD1sClEcusrLy1OKvboZHAjNSmduoJe5E3p\/83QGitP2AX54UhsF\/9X7vhNCCCGEEJKPBgrqhkjhTUxW7ZHeOkh9kXR5PJy03w\/PtoGChhSyHOYnbiA9RJZD2n8Y\/Ixfvhsos\/H9Yx8wqHCMMFT0+aK9uQdSgnQDxS1FSDr5QO3t7bxQFKKBUts\/pWRP0bGn8IisVB1RMmVH\/q5PStYXYyVOA0U3SDJp3bp1OWegAISCyXKs6kwIIYQQQvLZQMHkH+nrkh5x8+bNtNvbsWNHSo0JdL3UtW3bNut9pGDgNdQizNYEX+\/w4jYnAKiRKMthjhDGPPEzfvluoMzW9y9gnoZ6mbKPO3fuzJg7y3uICHJDr1Xj1GCE0EDJCQMFPyC9kw3C6tyEUKtcNFCkjgojUAghhBBCSD4bKLjnlYeDKMzpJWJAb9zgVevXr8\/aBB9FR2W53bt3uy6HJhGy3P79+wNP5v2OX74bKLP1\/eug9bFboWAUjZX3SktLXbehzx137drFC0UhGih1z6eUzo5Mmxq2Kfm6tCkWw0RSfORvMUwaRFJcNrnciYEppTgMFP1kR3ccP+SCgaLnSrIGCiGEEEIIyVcDBSkRknaCB4NeC56iMKs9DV8XosjlfhlNI\/BabW1t1ib46OQpKfdI97CneAioUSLbO3PmjO\/jCTp++W6gzNb3r4OoE9kHImJ0UOMkXedVQW93jZoohAbKrBooaB\/mBELagobK5YKBold1tv9YCSGEEEIIyRcDZevWrWkbNARFr0ExGzVQAIqPyrJomeuEdHhBzZJXr175Pp6oxy\/fuvBk+\/vX0TstObWq1tOI3OZ1ervrlpYWXigK0UA5OTCl1JiYVhIjpHHElGWMJA0R++vy95lEqsRwkZQf2Y9fAwVpN5nyyFDhGJWOZbnr16\/npIFiTx8S4KDKMkHzGwkhhBBCCJlNA+Xq1aspT+mdupnEOYFGtDomrRBqUUQ9wUdHzXTzAnTvlCgVpzSfTMcXx\/j5+XxBxq9QDBSMtR7lcuHChRnL3Lt3z3of7ZadKCsry2iyERoosRooeiEemCSdnZ2OIXP6CY0LF0K6\/v77b7UsaorAHMHFwN52LJsGCoR6LQjtwnFh2erq6pQ2yW7hgIQQQgghhOSygaI3RcB9bbq6hN3d3ZFPoBE1oHfmdAKp87gXFx0+fNhap7Gx0Xp9aGjI8b4c6SN6hIjULkTxWGk3jOgTp\/lOpuOLYvzCfD4v45eJsOMb5vvv7e1VdVEgdE2ygzbJSBWC0aWDObvelATtpzGPtIPjXbFiRUo3Jvn+8V8UtQ1b\/4YGSh4YKDV9E0q1\/R+V6gZMnRqcVJK\/6wdNnUiqfmDS1KCpk0nViZLr1fZ\/UKrpHlfya6AguuTHH39MMSFQcBXhcXZwMRHXV+\/nrb+GtlizZaCggrZ+XPpx4oLZ19fHXw4hhBBCCMlLAwXGgdcCoPaHmtkyUPQir5nk1BkTr8Hc0CfbeiQ8xsCtfW2m44ti\/MJ8vigMlLDjG+b7f\/DggbVMfX39jPfxmryPjjslJSXqu0ShXv240tWdQTFhqVEDzZkzRxk2egef5cuXG+\/eveNFggbK7BgoAOFP+BHrJzZOeCdgQuA9u5GCvuJoiYXCTjr79u2zlkH\/cTt6IaCDBw867lNvuzUxMeFqoMAVRbqOfmz4N8ygIDmShBBCCCGE5IqBYr\/\/TiekdvgBKfqy7qNHjxyXQdFOWQZdXZzQ03AyaWxszHEbmBzj\/t1ueCxcuDBtKYFMxxfF+IX5fPrxIZ0lCFGMb9DvH5kK6Qym5ubmtCYVvlMcfyYwx8d802kbeOCOAABSgAYKwr6gyq5\/lKp6xpVqeiaUqj\/\/G6rpnVCq7jN1rNdUdU+qaiyNKx3rNVXTM6ZU2fmPkuzXLwiLGhkZUQVXYahkOjERPjYwMKCWhzkxW6kxThEuODaYKfiB8gdGCCGEEEIKwUApNmCkoH5hR0eHikwohFT8hoaGtDU+8h10U0KUDCJaUN6htbVVRbQ4PUj38v2jjAS2A\/OGUSfhoIESsYGSr3jpwkMIIYQQQkg+QQOlMIFxgnnLxYsXORgkq+S8gYJoDqiio0tp\/6O3Sge63pn6a0yp4vE7pf2f\/63UldRjUweemqr4vA5U+WRMqeJxUp+3qdTepST7LRZooBBCCCGEkEKDBkrhgSgK1GxEXRfOW0i2oYFCA0VBA4UQQgghhBQaNFAKD9RAKS0tZW1GMivkvIGC\/C9IDA1JrZEir1FJtiv7kf0WC5mK1BJCCCGEEJJv0EApPKQlLyGzAQ0UGigKFJPCxYgXJEIIIYQQUijQQCGEREnOGyiEEEIIIYQQEgQaKISQKKGBQgghhBBCCClIaKAQQqKEBgohhBBCCCGkIKGBQgiJEhoohBBCCCGEkIKEBgohJErypohsIpFQevr0qZK9CGxfX5\/S69evlaamppQIIYQQQgghxQkNFEJIlNBAIYQQQgghhBQkNFAIIVGS8waKGCeHrvyldOTha6WqzldKNY9eKx2+k1CqabutdOfuPaXHjx8rZWpjLMaM7K\/Y2hgTQgghhBBSaNBAIYRECQ2UAjFQnj17Zty4cUNpcnKSZzYhhBBCCCl6aKAQQqIk5w0UMTbKbowobb6WMHXV1Jbro0rbr40old0aVdp164VS+U1Te+6+NHUnqeTf5Z+X1fV7+19KYqRkYnx83Hj79m1g\/fvvv5GM02+\/\/WZ88cUXSqOjozyzCSGEEEJI0ePVQPnvv\/+MoaEho7W11dMcQAcPMi9fvmzU1tYap06dMq5evWr8\/fffkX6OMMeH5S9evGjU19cbjx49Mj5+\/BjZ2N6+fds4f\/68cfz4cfXf+\/fvGxMTE1n9fLMxvnjQ7mWux\/GlgUIDxcbChQst4yKIcIGlgUIIIYQQQkj2DBRMKJ8\/f25cuHDB2Lt3r\/Hdd99Z99K3bt3ytG3cc5eVlTne43\/zzTdGdXW1MT09HXhCH\/b4UBdy5cqVM47t66+\/VpPxoCB6f9u2bcaXX37p+NlxrDBs8Bni\/HxhDZMw+\/\/55589zfVevnxZlONLA2UWkRSbRS3PlZadH1Ja2jiotLxpSGlly7DSmosJpZKLw6lqTSiVtifVNqxU0pZQWnthWGnZJVNi3GRCPxmD6MqVKzRQCCGEEEIIyaKBgvt8t\/tzLxPMd+\/eGT\/++KO1zrfffmts3bp1xsPVo0ePBjrusMf34sULY8mSJdY6S5cuNUpLS5V5Iq81NDQEOraTJ09a25g7d66xceNGo7y83Fi2bFnKcaYzacJ+vrCE3X+cBkohjC8NFAfGxsaMhw8fGiMjI0VtoCAUqr+\/f4bgCspJipPeaRkoSAgWDRRCCCGEEELCGygwFg4cOGBs377d1wQTxogsj\/t+6f6JJ\/9PnjwxFixYYEWiBEnZD3N8OIZVq1Y5Rryj\/ID+3oMHDwJN8EtKSlTtRT3CBvttampKicJ59epV5J8vSgMl6P7FQFm8eLHR3t7uqiDpUoUwvjRQHIB5gosFhCKtcRso1d3jSs0jn5RaR6eVWpK6YFPziKmLL0ydF42akvWabap6OqYk+w0KLpxyku7cuTP2L5IGCiGEEEIIId4MFBgeSHERzp4962uCKSYEojqcQC0U2R5SKfwS5vg6OzutZauqqma8j0k3Imbw\/o4dOwIdWzow95H9\/\/nnn5F\/vigIu38xUFavXh3LseX7+NJAcQDFksRAidNEKUYDBU4jTBAUDYLw73Q5bukMFFwgr127plxILw7o+\/fvjb6+PrXfjo4OdQGGU00IIYQQQkihGCh2\/E4wf\/jhB6sehdOEF8YF3kcdC9xfh8XP8ekTbDfzRl9meHg40jE\/d+6ctW0UP43688VBNg0UzPWQAgZlMksKZXxpoCSBaRK3iSJGxsnnU0pNiekUNYyYakyYEuOkMalzLpL3m5KS7dQNTCnNhoGCHxNObnGEdSH\/DSFbbkaK3UCBaWLPk8MFHCFhTttARWc40HpepF5oClW7CSGEEEIIoYGSWgMD8yD7Pb0YLG4RKnFN8FEeQIqPogaKG3q5gbq6ukjH\/NChQ9a2vc4hislAQSaH7AuRSsUwvjRQsmiiFIuBguiQTZs2pRgXixYtUtJfQ3EqJ6dSN1BaWlqMr776yrVwEJZNtz7WRb6cfRt37tzhL4YQQgghhBS9gXL37t2Uh5RnzpxRxgnqnaDridSoGBgYyOoEHxEnstyePXtcl0PEuSy3b9++SMcc8wjZNup40EBJBXVn\/Bog+T6+NFCyaKKIkXGif0rp9PC0Ul1SZxOmTg2bkr9lOXldDBJ5\/UzClGynIanawSmlbBsoJ06csJbHD1FvoYywOr3Y0+nTp9MaIBI1cuTIERVZgvQd\/Ft\/HxdNnYMHDxq\/\/vqr+sxSrAhhZXhd1tm9ezd\/MYQQQgghpOgNFKB3S4HWrl2rin9KV56bN29mfYKPe39ZrrKy0nU5dOmR5bZs2RLZcWJCr7fb9Zqikq8GCswzRP2jSCvG+969exnXRSORn376SQlZA8UwvjRQHOju7o7FRCkGAwV1SuBQy8UWHY7swMyQ1B781\/6d6QYKfkxOx44ftSwDZ9wL+sUVJg4hhBBCCCE0UMyuKJs3b3aM+IaRMRsT\/La2Nk\/1MVDjUJaDARAFqJGplyJATcW4DIxcMVCchLStnp6eyI8xn8eXBooLaGmsmygwVaIyUI4PTCmJISLGx+mEqVNJ1SdVN5SULJfU2aTsr8vfs2GgXL582VoWOW1u6AaI3o7MbqC4\/WAHBwcDXSgljQi5nIQQQgghhBS7gQLzpKamxnUSPWfOHOPSpUtZn+DryzlFrQuIXJDl5s+fH\/r40PHl+++\/t7aJQqdxGhizbaDAzIBJhoiQ8+fPqzQoeSAuY4oH0VGR7+NLAyUNyAcUAyWKsLViMFCOHTtmLQszxQ3dUUbIoJuB4tbGGBd6qWuC\/+p9xXXwY0cEEULKUE9FnE4aKIQQQgghhAaKoaI7ZPmKigoVLQ5DxV5DMCoTxevx6Q9mMcdw4+3bt9ZyK1euDHVsqLsiRXOR0tLa2hq7gTHbBorbmKJepZ7SlamTarGMLw0UF+y1UBCREpWBUts\/pWRP0bGn8IisVB1RMmVH\/q5PStYXY2U2DBTUFpFlUZXZDeTUyXK\/\/PKLbwMF6EVp8SMXYKbAoFm3bp2rk04DhRBCCCGEFLuBohdg3bVrV8okGfUtUM9QLzDb1dWVtQm+3uHlwIEDrsuh3qIsh\/odYSb3KB8gRXODPkAvBAMFIDVq8eLFnuZlxTS+NFCyZJ4Ui4Eilboz5UuiC45bJx2vBsq8efMcI1D0EEQUoN24caNy1vEjRQgaDRRCCCGEEEIDxVDpGrKskzmC7pp6JAKaMmRrgo\/mE14aQKDJhCy3f\/\/+QMeEOYc8nMX8Id2D4GIxUAAac\/jtklPo40sDRQOtujDJjsM80Q2UuudTSmdHpk0N25R8XdoUi2EiKT7ytxgmDSIpLptc7sTAlFI2DZTa2lpPKTwI13LrGe7FQNFzHfUaKFeuXElxoD98+JCynrioNFAIIYQQQkixGyhr1qyxHkhOTk46LgNjRbaHtrPZmuDjPl7SiHAP75ZCghoasj20YPYLanJIWgn256egaaEbKPrcLl0dmmIaXxooWTJPisVA6ejo8OQA626mveWVFwNFv5Dv2LHDer2srMx6HR2B7NBAIYQQQgghNFBM8CBS0nMQbeI2T4qySKuf49PnDEgpcmLTpk3WZ3C6\/8+EHmGDIqq5ZGDM9v5RakG2FaReSSGOLw2ULJknuoFycmBKqTExrSRGSOOIKcsYSRoi9tfl7zOJVInhIik\/sp9sGihv3rwx5s6da+W2OV3kcWETNxlpOChU5WaguBWr0lOF9Pw5FDiS1xHO53ZsqCbuVniWEEIIIYSQYjBQ9Ba2bvfdKG8gy+BhpX2egEYNkNcUDz\/Hh46c6eqgoIOMzCuc0nwyHR+6gcr28aAV88JsTvCDjF+2DAbM2fRWw0FqoMz2+JKYDJS4ap4Uo4EC9DA6uNq9vb3KrMAPBmMtIVxQc3PzjPV1A0WKzKLoEML2UMyourraem\/VqlUp4Xy6S11SUmI8ePDAGBoaMpqamqz6J5laJBNCCCGEEJIvBgpS23GvLDp8+LB1v9vY2Gi9jntiexqMnv6OB432yHDcu+stZ+2T\/MrKSus9FJx1IszxgQ0bNqREMMhDUBSPlWND9InTfCfT8elNKTCvQJqKm7q7uyP\/fF7GLxNhxxcZBDCidHMDy+sPpjdv3uy4b8zz1q9fr+RUvmG2x5dkwUCJyzzRDZSavgml2v6PSnUDpk4NTirJ3\/WDpk4kVT8waWrQ1Mmk6kTJ9Wr7PyjVdI8rZdtAwY9P78Yj+W4oGKS\/tmfPHuPTp09pDRSp1AzpDiiEHyQqh+sg6gQXUKfOO1gfdVH0ArNHjhzhL4cQQgghhOStgaIXUc0kvXOloBeSlUgBNGFAvRP99T\/++MO3QRHF8eE1TL71NKIlS5akdAdqb2933Hem43ObNzjJrQZImM8XhYESdnxlDJA9gC6meACujwsefiOS3wk8rJbl6uvrc258SUwGCib8cK3GxsZiPcB8NVCwrpyU5eXlntdDGKDuWItwwUM4lxtioMAFR8QJ0nX0PvT4N3Id3XIc4Zzrxgt+uFgelbxh2Ojt2HAxJoQQQgghJF8NFD3NJZOc5juYCyGFZOHChY7rwEjB\/bXT0\/1Dhw5ZyyFiwYmwxweQ8o\/7efuEHMd8\/fp11zHLdHz6HCOTkDoS9efTjw\/mRRDCjq\/bGKDkwYkTJ4yJiQnXfXd2dqY1QGZ7fElMBkq2QFgSVNn1j1JVz7hSTc+EUvXnf0M1vRNK1X2mjvWaqu5JVY2lcaVjvaZqesaUKjv\/UZL9zha44ME5xDGk+wHqyyPHTjdIELqFEDH8gNwKXOmgijhC0RA9Y98nLv54zx6qRv6vvfOAkqJK3z7GdQ0rKqY1bFJZ87qIrq7huLu6BlBhEcGAAgoiWeIgA5JzFgRFomRFcpAMMszA5ARIkCQMDHx\/GXBwQL3fPLf6lrdrujpW9fQ0z++c58B03aq6XV1dXfep974vIYQQQgipaAaKU+AeG\/fbMEswzR55Bvft2xdT98sYJ6BfmHaCh6PxMGVjyZIlcvA\/YcKEctk\/xlwYM2E6l\/rcMU6yVjMlNFBooBBCCCGEEEIqBG4bKKR8gHECAwXpJAiJJjFvoCC\/CjTh63ypiXnHpSblFxnafkJqwtYiqYml\/5fK92iroUnfGJpQug40edsJqQlbPSrdptSGfCm1X0IIIYQQQkjFhAZK\/IEpMMg9grwu4VS5ISQSaKDQQCGEEEIIISQuoYESfyAHSt++fW1zOxLiJjFvoGAeGaQMDTW1RiV5dUpqu2o\/ar+EEEIIIYSQigkNlPhDlWQmpDyggUIDhRBCCCGEkLiEBgohxEli3kAhhBBCCCGEkHCggUIIcRIaKIQQQgghhJC4hAYKIcRJaKAQQgghhBBC4hIaKIQQJ6lwBgqSBtmJEEIIIYQQQhQ0UAghTlIhDBSYI2fOnBE\/\/PCD1IkTJ8oIr5eUlNBIIYQQQgghhEhooBBCnCTmDRQYIqiGMyMpT7w+O1s0+GKneGPuLtFg7jdSjRfsEo3m7xQN5+SKfgs2i+PHj0uzhUYKIYQQQgghZzc0UAghThLzBgrMkMOHD4vnJ6eJZ6ZvFU9\/tk08NaVUk\/Oknp2+TTw3Y5uoUfpvrVnbRetpG8WxY8fMaJRQRQghhBBCCIkPaKAQQpwk5g0UTM3ZuXOnePjjLFFtbLa4Z3ye+Ou4HHHbR1lSf\/0kW9wxPlvcNTZLVB+fIx6Zki\/qTE4W8zbliQMFR2VEiq8pP3bTgKB4iGBJSUkRM2bMkMJ7IoQQQggh5GyDBgohxEkqhIGSl5cnqo7KFDcNzxBXj8oSlUdkiUuHZkhdNjLT0OB0cUXp39d\/mC1uHZcr7v10q6g2cZt48DOPphiqPnGrVLUpeVL3l74GPTRlq3h0ar54cmqGmLQ+R04bCsVEwTEsKCiQ0TKBgKmDtoiUCRcYPtgG9Msvv5RZXr9+fVGpUiWpbdu2hby+U\/0ghBBCCCGkvAjWQMF9bHZ2thg5cqTIz88PeT+Rru\/m9tF+9OjRIjExUSxfvlycPHnSkT4dOHBAzJkzRwwZMkS0b99e\/rtw4cKwDCu3j5+b+8fMhy1btogxY8aItm3bil69eonJkyf7HINFSnFxsTn2wkP\/QHz\/\/fdixYoVYuDAgaJbt25i0aJFEY1BSQUwUDBAz8rKElVGZYpzh2SISn03i0q9UkSlbh71SjPUfbPU+X1TxcUD0kTlUlUZlC6uHmDomuGZhoamS11b+n\/o6kFphgaXvj4sU1w3MkfcPWqzNEKCOSkVf\/jDH0zDIpCJ8uSTT8p21113XdjHRTdItm\/fHrKBEmh9p\/pBCCGEEEJIrBkoGDBjjDFixAhRq1YtcdVVV5n3tLNnzw5qwB3J+tHY\/rfffiuqVq1qrqd0wQUXSLMjXNauXSueeOIJce6555bZNoS+wrDx93DV7eMXrc8PxsRvfvMbn8cB+u9\/\/+tYnzE2feihh8xtL1myxG\/7uXPniiuvvNJnv9q1a8eH3\/FuoJwzJF1U6pMqKnXdJColJIlK7T1K2GyowyZDXZINdUoy1G6TofdTDCVsMqT+7phkqLTteV2Txfk908RFg9LktKFQpr6EYqD85z\/\/cdRACWSQhLPcqX4QQgghhBASawbK+vXrbQe9wQygI13f7e3v2LFD3HTTTeY6GKs88MAD0jxRr\/Xp0yesvnXp0sXcxqWXXir+8Y9\/iJo1a4o\/\/elPXv30Z9K4ffzcPr6IOmnYsKG5Dsyke+65R9SuXVtUr15d\/Pa3v5WvP\/roo471OSEhwauf\/gyUfv36me0uueQS8a9\/\/Us89thjZr+gunXryvdBomSgwNjIyckRhw4dioqBcuOIbNFszTGRmH5S9Mo+JbplnBLdM0+JPlmGeuf8KNU355ShLENqec9MQ\/1yDfXNNtSntG3v0n+7l24vYctJ0WT9MXHDmBw5bag8DJRZs2aJF154QV7gMjIybLcxePBg8cwzz0gdPHgwZGMj0PpO9YMQQgghhJBYNVD++Mc\/ijfffFMOMMMxUMJd383tI7LgjjvuMNtPmjTJXIbpG\/oyTOkIx0CpVq2azLX4008\/ma8j\/QEiMtS2L7zwQrFr165yOX5uf36tW7c22993331ySo0OxpFffPGF+PDDDx3p78qVK8tE\/NgZKBj74dijDUydPXv2mMv27dsn7rzzTnMbmHZEomSgwDxZs2aN1NatW103UBquLBTvp58SvfJOi\/5bz4jeeT9JDcw3NGCroUHbPMo3NHCroX55hgZvNzRoq6GB2wz1zj0jumefFp0yT4kGawrlPrHvaBsoCKdS21m9enXYxy3SyBCn+kEIIYQQQkisGSjId6gPLHv37h3SADrS9d3c\/rJly8y2LVq0KLMcpsZFF10kl2NcEirIw+EPPFhV+0cukPI4fm4e36NHj4qLL75Ytr311ltdT1J85MgRcf3115eJlLEzUDAlSbVJTU0ts3z\/\/v2mwXLLLbewEm20DBTMqVMGipsmijJQmm8qEp0zTomuWSWiR84Z0TfXUM98Q73yDPX2qHuOof75hj7wqEeuoZ55hrrnGsL\/E3NOi47pxeLdpONRN1Dg3mK9Jk2amNtBUia8Blk\/I\/RNLQs3iayv9Z3uhw4SVsHtnTBhgkhKSgp48VU\/elhn6tSpMioG68E55Zw9QgghhBASroFiJdIBvNsGQCjbf\/bZZ822mZmZAU2O3NxcR\/uqTx9BctlYOH5O7v+DDz4w2yIaP1SQy0SNnwKNhzDmUZ\/n+eefLyNaAhkoymy56667bLfbqFEjczvIlUKiYKAAmCZumyjKQGmadEK0TTsl2qeXiM6ZZ0RilqGEHENdsg2971HHTEPKSOnkUecsQwnZhjpmGcL\/O2SeFm1Si0WTjUVRN1C++uor23l4yh3UeeWVVyJKImu3vtP9AD\/++KM0ZM477zyvbcH5HDdunO3Fonv37nJepa9+2LnZhBBCCCGEnK0GCsZ155xzjmx344032rZDkle1va5duzraV\/1BLCr\/xJuB8vvf\/162w9jGOnUnGBYvXmzuC5Vx\/IE8Mqpt\/\/79RVpaml8DBZ9\/MAlsP\/30U7Ndhw4deKGIloESDRPFnMKTVCTeTS0W7dNKRKeMM6KjR22zDHXIMJSQaah9lqF2mR5leatDpqFOWYbeK1XrjBLRMqVYvLUh+gYKSoqFYly4lUTW6X7gB+v+++8321x22WXiz3\/+s9ccPlwMrOjOLlSlShWZmArOKw0UQgghhBBCA6UsiDhR7V588UXbdsnJyWa7V1991dG+6uOi6dOnx5WBgtwmqh3GNAqUC166dKnMKYMpMv5Am2AMJky\/UVNtYIbgAXMgAwWFUNRy5HWxQx\/zvfzyy7xQRNNAcdtEOVsMFIRywcHUHVt8eVWdb+tn5JaB4nQ\/9FwqyBytEk3hHFHHrHLlyuL48ePmOpiHd\/XVV5vObkpKitdFC7XMMYWMEEIIIYQQGii\/snDhQrMdpmnY8c0335jtUJ3FKWCY6OWMg5myX5EMFIx3VDtUH4Jh8eCDD5ZJ8IpErTA77IyRp556Suqzzz6zHQPjwbUaM6rxZSADBWMtld\/mhhtusE17MHHiRHM7Dz\/8MC8U0TZQAEpluWGiKAMFpkarlGLROrVEvJd+RrT0SBkgbTIMqSk5aAO19ahztiH1evtMQ2o7nUvVoXTbLdKKRePkophPIut2GWMn+gGTQ9VFx4XFCjKCq3VHjhxpvq6Hnvmbu0cIIYQQQggNlF9BVZVg8o+gGo9qhyhvJ9i8ebNXmdzPP\/88Zo6fU\/vX0x3ggS+i5PH\/yy+\/XI5blHmhcpZguk44vP7662Z55FWrVpmvBzJQAKqo6rksreCBud7m5ptv5oWiPAwUgJLGuokCU4UGytlroHz00UfmsqFDh5ZZF8lg1fKmTZt6LdPr1iMRFb7ohBBCCCGE0ECx336vXr3MdpgSbweq0Kh2GPxHCira6JVicP8eS8fPqf2jGIYeaXLJJZeIsWPHmpEeKvejWo4xTShjSjBlyhTb\/DTBGCj69JxrrrlGbNy40VxWWFgo6tSpUyZahpSTgQIyMjJMA2XLli2OGSiNNhaJ5luKReu0EvFexq+GSYcsQ2pqjprS0ybdI9XOo84etck0pF5Hm5al226Reko03nSCBooD\/Wjbtq1XJAlKaumqWbOmuRyZwHWQzEj\/YletWlVmnUYlH0IIIYQQQmiglAUFGlQ73M\/bgan5qt1tt90WUd8wblKJVZHAVo8sjzcDBVNu9DEKKoX6AtN7VJtPPvkk6H5gapUqovHoo4+a6Q9CMVAAHk6rdvhM\/vrXv8qoE1V+Gflb1HJU+SHlZKBYc6EgIuVsNFACZWM+WwyUGjVq+E1Iq+tvf\/ub17rIg9KwYcMy7ZBNHDlQCCGEEEIIoYHiDQbVqh3upe3Iz8832\/373\/+OyDy58sor5XYwdX\/mzJmuvr\/yNlDWrl1rtkPEjR16LpjWrVsH3Q81ToQeeugh8dJLL3npiSee8Mpdgte6dOnic1uYDaBPqYLwN6KU9P69++67vFCUh4HihnmiGygN1xeJd5OLZRnjTplnpIkC4f+QMkzUlJ12GYbae6SMlvbphlp7pNZHQtp2aSWieWqxaLQp9Ck899xzj3kS5uXl+W1bvXp12e4vf\/lLXBso+EKrZfXq1ZNhhHZC6JsVhMKhLnm1atW8vvhwUXv27MlvLyGEEEIIoYGikZOTY7Z7\/vnnbdutX7\/ebPfGG2+E1Sfc+2OKCLZxwQUX+I2IiBcDRa9ygzGdHenp6Wa75557LqwxZbD6+9\/\/brs9FOBISkqSUTAwfzDFCAwePNhcf+DAgbxQRNNAQW4KTNVxwzzRDZTGG4pEi5Ri0TGjRCRk\/ZrDBP+HlKGijBNVZaejR52yDZnVezxS63dG+\/QS0TKtWLwVRg4UXKDUSYh5Z\/5AFIVdaal4MlC6d+9uLkOt+UjARV6\/oKA6Dy5ghBBCCCGE0EAxKCoqkslL0e7aa6+1rcKCHCVqe+E8mESxCFR5UffloSSMrcgGCgwI1c7XbAIFxpKq3dtvvx10P5D415r2QNd9991nbhdFOvBap06dQn6\/eqQLxlkkSgaK2+aJbqA02XRCtE47JdpllYiOOb9OvVGGSIdsQwkeKYPlPY9UOWOVZFZFnihjBW1ap5eI5puLRaMwyhi3atXKPAl79Ohh2+7o0aPmRe3NN9\/0a1wsWLAgJgyUcPsxa9YsR0ID9fMNF4pQ68oTQgghhBAaKGeDgQJefvlls61dTsp\/\/vOfZpWX3bt3h9yfxx9\/3G+xiHg1UIA+jWb79u0+22D8pNoMGzbMsX4i50owOVD8gTEbIvpZwjjKBko0zBPdQGmefFK0T\/9RdM4uEV1yz4iETENdPErIMZToUecsQ6Zh4lGXbEPvZ3mUYwht2meUiDZbTommG0PPgaLPI0NJK7tkp\/379\/d7scF0FrW8TZs25WagONEPHD+9ms60adOCPp7WhEkKmE5qe8iCTQghhBBCSEUyUBDdPXz4cKlgHwiGsv2vv\/7abOvrgW1KSoqMGrGb5hOof5MmTfKKwlDTQqJlYIRz\/Jzc\/\/z58822KDfsi6efftpsk5qaGjMGyunTp70KeYRbZpkGShgGils5T+wMlKYbi6S50SWjRCRqkSaJuYZUmWKYK11yf31dteuWa0hFnCh1zTXUQZY+VgZK6BEoSHp69913mycj\/o+LkzICMP8MX3IkV1IlpXyZLDNmzPBKmLp06VK57WgbKE71Y968eV610BFitnfvXhlOiGMD1xaurB61g+3AhEIlHnwO+KIXFxfL0EB1\/OCaolwaIYQQQgghoRooKOOL+0ylZs2amfesAwYMMF\/Pzs72OQ0mkvUbN27sVanSF5H2T4\/axkNbPPwGyNWoyg3jfhp5MawE6h\/u09Xy22+\/3W+eww0bNjj+\/oI5foGIZP8YE91yyy1e1Y7U8cX4JiEhwW9+GeQkQd4S6OOPP3bcQDly5IisXoqKPjrfffedrLij1n\/sscd4gSgvA8Ut80Q3UP63aK94ZfkB8dbKw6LpmiOi8ZoCqXfWHZF6a42ht9cZemddodRbaw5LNSt9DWq8ulCqkUdN1h+WenP1YfH6ioLSfXwnai\/aF7KBAlauXFkm0zEG\/CjrpcKk1MUKJcZ8AVPl5ptv9trG7373uzLhVW4bKE71A+DiqVxuPQO0mspk\/QLj3NLbop0yTpQwP5AQQgghhJBwDBQ9iWog+aqwGcn6wRgAkfYP4zOYG6rN5ZdfbuZhVFN37MrrBuqfPq4JJLvUBm4fv0BEenyRrFflgIFQHhiGyBVXXGG+hoIhMDOsLFq0yGyTmJjouIGiJ7BFhBDKF+tli6E6derIB9QkigYKXLb9+\/eHbDKEa6A0WP9\/4u3kE+LtjSdFs+Ri0XKzodaphlqmGGqVaqjZFkOtUjxKPSXVfIuhllgn9dftNCtt83ZS6fY3FIkGq\/5fWAYK2LFjh9e8OKtuvfVWaTr5A2Fed955p9d6qESj89prr5nLsE8rkS53qh+K5ORkcf\/993uZJhDqnKNOOhJZKXChefXVV8Udd9xRxniBY46KPXYJsQghhBBCCAlkoGzcuDHoAXRhYaGj6zdp0sRcdu+99\/rsd6T9A4cPH5YPP62GR5UqVfxOrQ\/UP+v9vD9haozT70\/vH5KqhoMTx3ffvn1lqoXqU6fsxpKI7Fft8KA5FPC5BSpcghLV1jGUEmZBdOvWjWOp8jBQooUyUGrM3S5qL\/5WvDh\/t\/jfwj3i5cWG6i\/bJ1Vv0R4p9fdLS\/dI1Vu0z9AyQ3WXGqrnUd3SbUB1SrdZq3Tbtb7cLWrM2ha2gaKA6wujBPW3R4wYIeeXIYzKLreHFbRDJMZXX30lc824bVRFqx8Il0tLSxMrVqyQCasCfXkx9WnTpk1i1apV8vip8DhCCCGEEELCNVDOJvBwEsUdPv30Uxk5EQ+D5z59+khDoEGDBuXeFxhVSFuAtATLli2Tf5c3GEMhFw7y1aBkMdIzhJMsmFRQAwVf9Jqz80Xt5YdFnZVHRd1Vx0T9FYbqriyUqrfqqFTdNcekXltl6OUVR71Uz6P6petAr64yVH8llheKWosKRI1peXKf5WVaEEIIIYQQQiKHBkp8AuMEBsro0aN5MEhUiXkDBe7Zzp07RafxX4rnPt8uaiw9JGouLRAvLCoQLy4uEDWXHJR6ofR1qObyAqnayww9v\/iQoUWGXlhs6MXSdaDayw6KWkvx\/9LXS\/9+bto20WHcXBntgH0TQgghhBBCKiY0UOIPTIFBfkTkdbHLwUiIW8S8gVJSUiLDoDZv3ixDo8ZOnCqGjp0ohnw0ITSN88hmObaJbWMf2Bf2iX0TQgghhBBCKiY0UOIP5ECpXr06p6SQciHmDRSUiUIkCAwNRIUgNwkyCyPBqZPCNrFt7AOZlrFPX2V7CSGEEEIIIRUDGijxB3MikvIk5g0UACMD0SAwNY4fPy6lLoZOSW0X+8C+aJ4QQgghhBBSsaGBQghxkgphoBBCCCGEEEJIqNBAIYQ4CQ0UQgghhBBCSFxCA4UQ4iQ0UAghhBBCCCFxCQ0UQoiTVDgDBblJ7EQIIYQQQgghChoohBAnqTBJZJFtGQleoRMnTpQRk78SQgghhBBCdGigEEKcpEKUMT516pSYkZQnXp+dLRp8sVO8MXeXaDD3G6nGC3aJRvN3ioZzckW\/BZtlJR2YLTRSCCGEEEIIObuhgUIIcZKYN1Bghhw+fFg8PzlNPDN9q3j6s23iqSmlmpwn9ez0beK5GdtEjdJ\/a83aLlpP2yiOHTtmRqOEKkIIIYQQQkh8QAOFEOIkMW+gYGrOzp07xcMfZ4lqY7PFPePzxF\/H5YjbPsqS+usn2eKO8dnirrFZovr4HPHIlHxRZ3KymLcpTxwoOCojUnxN+bGbBgTFQwRLSkqKmDFjhhTeE4lP3Pqcef4QQgghJB6ggUIIcZIKYaDk5eWJqqMyxU3DM8TVo7JE5RFZ4tKhGVKXjcw0NDhdXFH69\/UfZotbx+WKez\/dKqpN3CYe\/MyjKYaqT9wqVW1KntT9pa9BD03ZKh6dmi+enJohJq3PkdOGQjFRcAwLCgpktEwgYOqgLSJlwgWGD7YB\/fLLL2WW169fX1SqVElq27ZtIa\/vVD8qCuozUUIEUzCcPHnSa71oE+hzjrXtEkIIIYREE38Gyr59+8TixYvFgAEDRMeOHcWwYcPE7Nmzw7pHx31wdna2GDlypMjPz3f0PWB7o0ePFomJiWL58uXy\/jMU8GDs448\/lu+xW7duYtKkSWLv3r0R9+vAgQNizpw5YsiQIaJ9+\/by34ULF4ZlWLl5\/KK5\/+LiYnNcgIfy4VJUVOQ1xrBTMGBcs2XLFjFmzBjRtm1b0atXLzF58mTe48ergYIBelZWlqgyKlOcOyRDVOq7WVTqlSIqdfOoV5qh7pulzu+bKi4ekCYql6rKoHRx9QBD1wzPNDQ0Xera0v9DVw9KMzS49PVhmeK6kTni7lGbpRESykn\/hz\/8wRxwBjJRnnzySdnuuuuuc2SAu3379pAHwIHWd6ofFYWnnnrKfB9Q7969g1rvhRde8Fpv9+7dNFAIIYQQQmLYQIGhcPnll3vdw+n6zW9+I80Gfw9TMeDGGGXEiBGiVq1a4qqrrjLXhwnjBN9++62oWrVqmf5dcMEF0qwIBO7N\/\/vf\/\/p8jxdeeKFo1aqV+Omnn0Lu19q1a8UTTzwhzj33XJ\/bxrGA4ePv4Wo0jl8gw8Tp\/WPs+NBDD5nbWbJkSdj9q1Onju35qQszNfwxcOBAeT7brY\/zg8SpgXLOkHRRqU+qqNR1k6iUkCQqtfcoYbOhDpsMdUk21CnJULtNht5PMZSwyZD6u2OSodK253VNFuf3TBMXDUqTJ2MoUxdCMVD+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","width":510} 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","width":506} 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","width":513} +%--- diff --git a/Murat_inputToba.mlx b/Murat_inputToba.mlx deleted file mode 100644 index b15abd1..0000000 Binary files a/Murat_inputToba.mlx and /dev/null differ diff --git a/README.md b/README.md index 5b46197..679ac2b 100644 --- a/README.md +++ b/README.md @@ -3,22 +3,26 @@ MuRAT - Multi-Resolution seismic Attenuation Tomography ![MuRAT is a code for attenuation, scattering and absorption tomography.](./img/muratlogo.jpg) -[![test](https://github.com/deconvolution/MuRAT/actions/workflows/run_test.yml/badge.svg?branch=with_test)](https://github.com/deconvolution/MuRAT/actions/workflows/run_test.yml) +[![test](https://github.com/LucaDeSiena/MuRAT/actions/workflows/run_test.yml/badge.svg)](https://github.com/LucaDeSiena/MuRAT/actions/workflows/run_test.yml) +[![GitHub releases](https://img.shields.io/github/release/LucaDeSiena/MuRAT/all.svg)](https://github.com/LucaDeSiena/MuRAT/releases) +[![DOI](https://zenodo.org/badge/64924529.svg)](https://doi.org/10.5281/zenodo.13996751) +[![License](https://img.shields.io/badge/license-EUPL%201.1-blue.svg)](LICENSE) +[![MATLAB](https://img.shields.io/badge/MATLAB-R2025b%2B-orange)](https://www.mathworks.com/products/matlab.html) -MuRAT is a Matlab Package for seismic Attenuation, Scattering and Absorption Tomography using Body and Coda Waves at multiple frequencies. +MuRAT4.0 is a Matlab Package for seismic Attenuation, Scattering and Absorption Tomography using Body and Coda Waves at multiple frequencies. MuRAT measures seismic attenuation, scattering, and absorption from passive and active data and models 3D variations of these parameters in space. The group of active users (providing questions, feedback, and snippets of code) is the [Volcano Earth Imaging group](https://www.lucadesiena.com). -The last release of the code is: +If you are looking for MuRAT3.0, the last stable release can be found at: -Luca De Siena, mreissuf, Yi Zhang, GeoSeisUtilities, Aqeel Abbas, WMZ, Cheng Qingyang, Ferdinando Napolitano, & SimonaGabrielli. (2024). LucaDeSiena/MuRAT: 3.24.10.26 (v3.24.10.26). Zenodo. https://doi.org/10.5281/zenodo.13996752 +Luca De Siena, mreissuf, Yi Zhang, Donato Talone, Aqeel Abbas, WMZ, Cheng Qingyang, Ferdinando Napolitano, & SimonaGabrielli. (2026). LucaDeSiena/MuRAT: Legacy MuRAT3.0 Code (v3.26.01.20). Zenodo. *Documentation* ------------- -The file Documentation.pdf in this folder serves as complete documentation for MuRAT3.0. This README file and the *Input_.mlx* files in this folder act as additional documentation. +The file Documentation.pdf in this folder serves as complete documentation for MuRAT4.0. This README file and the *Input_.mlx* files in this folder act as additional documentation. The Wiki for MuRAT is under construction, but you can already check a bit of the history of the code. @@ -27,7 +31,7 @@ We recorded a Video Tutorial! Just go to the [Volcano Earth Imaging group page]( *System* ------------ -The program works on Mac, Linux and Windows systems with Matlab version R2017a or higher. +The program works on Mac, Linux and Windows systems with Matlab version R2025b or higher. Necessary Toolboxes: Signal Processing, Curve Fitting, Image Processing and Mapping. The Parallel Computing Toolbox is recommended for speed. @@ -35,10 +39,9 @@ Custom toolboxes not included in standard Matlab installations are also provided 1. Routines to read SAC files created by Zhigang Peng and available from [his SAC tutorial page](http://geophysics.eas.gatech.edu/classes/SAC/). 2. The [Regularization Toolbox](https://www.mathworks.com/matlabcentral/fileexchange/52-regtools?s_tid=prof_contriblnk) was created by Per Christian Hansen and available from Matlab File Exchange. -3. The [IRTools](https://github.com/jnagy1/IRtools/tree/ebd70d4036c3cd8c82fc1e17033351491fddf11f), included in MuRAT as a zipped folder. -4. Functions from the [Geometry and Image-Based Bioengineering add-On for MATLAB](https://github.com/gibbonCode/GIBBON). +3. Functions from the [Geometry and Image-Based Bioengineering add-On for MATLAB](https://github.com/gibbonCode/GIBBON). -Three sample datasets (Mount St. Helens, Romania, and Toba) are included and allow the user to obtain sample models. The datasets work with the three corresponding *input.mlx* files that show examples of what the user can get with the code. +Three sample datasets (Mount St. Helens, Romania, and Toba) are included and allow the user to obtain sample models. The datasets work with the three corresponding *input.m* files that show examples of what the user can get with the code. *Instructions in a nutshell* ------------ @@ -49,26 +52,24 @@ The current version works following these steps: 2. Work in the downloaded folder after moving it to an appropriate location on your system. -3. Check that the IRTools have been downloaded as a zipped folder in the corresponding folder in the working directory. Otherwise, download them from . +3. Open one of the three input .mlx files, providing a step-by-step explanation of all inputs (*Murat_inputMSH.m*, *Murat_inputRomania.m*, or *Murat_inputToba.m*) and create your own. -4. Open one of the three input .mlx files, providing a step-by-step explanation of all inputs (*Murat_inputMSH.mlx*, *Murat_inputRomania.mlx*, or *Murat_inputToba.mlx*) and create your own. +4. Use a velocity model, storing it in the corresponding folder. The format is [Latitude, Longitude, Altitude (meters)]. If haven't one, you can use either iasp91 or Lithos, included in the package. -5. Use a velocity model, storing it in the corresponding folder. The format is [Latitude, Longitude, Altitude (meters)] - -6. MuRAT works with [SAC files](https://ds.iris.edu/files/sac-manual/) that must be stored in a single folder and corrected for the instrument function. The files must have populated headers. Your SAC headers get tested anyway; the result is shown in an Excel file. The code takes from the header the following fields: +5. MuRAT works with [SAC files](https://ds.iris.edu/files/sac-manual/) that must be stored in a single folder and corrected for the instrument function. The files must have populated headers. Your SAC headers get tested anyway; the result is shown in an Excel file. The code takes from the header the following fields: ***a)*** The P-wave picking in the reference time of the waveform (in seconds); ***b)*** The coordinates of the event in degrees - beware, *the earthquake depth must be in kilometers*; ***c)*** The coordinates of the station - beware, *the station elevation must be in meters*; ***d)*** The origin time of the event (optional) in seconds. -7. Run MuRAT3 and select the name of the input file desired. +6. Run MuRAT4 and select the name of the input file desired. *Workflow* -------- A. ***Start from the Murat_input..mlx files*** -The input files are self-explanatory and provide detailed descriptions of every input and references to papers you can use to set them. If you have a 3D velocity model, use *MuRAT_InputMSH.mlx* otherwise start from either *MuRAT_InputRomania.mlx* or *MuRAT_InputToba.mlx*, the examples for 3-component data. +The input files are self-explanatory and provide detailed descriptions of every input and references to papers you can use to set them. If you have a 3D velocity model, use *MuRAT_InputMSH.m* otherwise start from either *MuRAT_InputRomania.m* or *MuRAT_InputToba.m*, the examples for 3-component data. B. ***Read the Documentation*** @@ -84,7 +85,7 @@ Beware, *.fig* figures are created with the invisible option in Matlab. There ar (1) Use the function *openfig(..,'visible')* to open them from the command window. -(2) Click twice on the figure file and write *shg* in the command window. +(2) Click twice on the figure file and type *shg* in the command window. All the figures are stored in subdirectories in the **Label** folder, created in the working directory: @@ -119,12 +120,10 @@ All the figures are stored in subdirectories in the **Label** folder, created in ------------ -* ***RaysKernels directory*** +* ***Rays and Kernels directory*** ------------ -*Clustering.tif*: This figure shows all rays used on the map (black, discarded) with those after declustering (red). - *Rays__.tif*: These figures show how rays develop in 3D for the Peak Delay and Q measurements. It plots them on three slices (WE, SN, Z). The fourth panel shows the location of the area on the Earth. *Kernel__.tif* and *Kernel__.fig*: Each *.fig* figure has two panels showing the sensitivity kernels in the entire 3D space (left) and the normalised kernels in the chosen inversion grid (right). This reduction implies several hypotheses: the most important is that most energy is still in the grid (the difference is generally < 1% if all sources and stations are in the inversion grid). The *.tif* figures are sections in the WE, SN, and Z directions. Figures are produced for all frequencies. @@ -151,6 +150,8 @@ All the figures are stored in subdirectories in the **Label** folder, created in *Qc__.tif* and *Qc__.fig*: Coda attenuation maps in 3D (*.fig*) and across sections (*.tif*). +*Qc_analysis__*: Relationship between coda attenuation and frequency. + ------------ * ***Spike directory*** @@ -170,13 +171,13 @@ All the figures are stored in subdirectories in the **Label** folder, created in ------------ +*Clustering.tif*: This figure shows all rays used on the map (black, discarded) against those after declustering (red). + *Qc_Analysis__.tif*, *PD_Analysis__.tif*, and *CN_Analysis__.tif* Three figures to evaluate the appropriate peak-delay and coda inputs. Read the documentation for further clarifications. -*L_curve__.fig*: L-curves and cost functions (depending on the inversion method) for the Qc and Q inversions necessary to set the damping parameters. The user can ask for a prompt or set the damping parameters. - -*Qc_analysis__*: Relationship between coda attenuation and frequency. +*L_curve__.fig*: L-curves for the Qc and Q inversions necessary to set the damping parameters. The user can ask for a prompt or set the damping parameters. They only appear when using a Tikhonov inversion. *Velocity_model.fig*: The 3D velocity model is also available as a figure in Matlab format. They can be loaded in Matlab and show the vertical and horizontal slices defined in *Figures Sections*. @@ -185,9 +186,9 @@ Three figures to evaluate the appropriate peak-delay and coda inputs. Read the d *Citing MuRAT* ------------ -The last release of the code is: +Please cite the last stable release of the code in your data and software open-access statement: -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13996752.svg)](https://doi.org/10.5281/zenodo.13996752) +[![GitHub releases](https://img.shields.io/github/release/LucaDeSiena/MuRAT/all.svg)](https://github.com/LucaDeSiena/MuRAT/releases) If you use MuRAT for your research and publications, please consider mentioning the GitHub internet site and citing the following papers, depending on the techniques you are going to use @@ -197,7 +198,7 @@ If you use MuRAT for your research and publications, please consider mentioning 2. De Siena, L., G. Chiodini, G. Vilardo, E. Del Pezzo, M. Castellano, S. Colombelli, N. Tisato, and G. Ventura, 2017. Source and dynamics of a volcanic caldera unrest: Campi Flegrei, 1983–84. Scientific reports: Nature Journals 7, 8099. - *Recent implementation of the Coda Normalization method with correction for coda attenuation variations* -3. Sketsiou P., L. De Siena, S. Gabrielli, F. Napolitano, 2021. 3-D attenuation image of fluid storage and tectonic interactions across the Pollino fault network. Geophysical Journal International, 226(1), 536-547. - *Most recent application of Q imaging with MuRAT* +3. Feng, Y., Ai, Y., De Siena, L., He, Y., Jiang, M., Mon, C. T., et al. (2026). Seismic attenuation tomography in Central Myanmar and its implications on continental subduction and arc magmatism. Journal of Geophysical Research: Solid Earth, 131, e2025JB032147. - *Most recent application of Q imaging to lithospheric scale* **Qc and Peak Delay (Absorption and scattering)**: @@ -211,19 +212,14 @@ unrest. Geophysical Research Letters, 44.4 pp. 1740-1748. - *First implementatio 4. Sketsiou P., F. Napolitano, A. Zenonos, L. De Siena, (2020). New insights into seismic absorption imaging. Physics of the Earth and Planetary Interiors, 298, 106337. - *Comprehensive review of the method and future outlooks* +5. Napolitano, F., De Siena, L., Amoroso, O., Ágústsdóttir, T., Benediktsdóttir, Á., Palo, M., et al. (2025). Scattering and absorption imaging of the Hengill high-temperature geothermal area, southwest Iceland. Journal of Geophysical Research: Solid Earth, 130, e2024JB030731. - *Most recent application* + *Disclaimer* ------------ Although we have cross-checked the whole code, we cannot warranty it is exempt from bugs. The package is provided as-is; we will neither be held responsible for any use you make of it nor for the results and conclusions you may derive using MuRAT. -*Licence* ------------- - -MuRAT is released under EUPL v1.1 - *Funding* ------------ -Some developments of this software package were funded by the Deutsche Forshungsgemeinshaft under grant number SI -1748/4-1. - +Some developments of this software package were funded by the Deutsche Forshungsgemeinshaft under grant number SI 1748/4-1. diff --git a/README_Murat_envelope.txt b/README_Murat_envelope.txt new file mode 100644 index 0000000..97193bc --- /dev/null +++ b/README_Murat_envelope.txt @@ -0,0 +1,43 @@ +# README – Using `Murat_envelope.m` + +## Overview + +The function **`Murat_envelope.m`**, located in the `bin` directory, computes the envelope of seismic waveforms used during the MuRAT preprocessing workflow. + +To correctly run this function, an additional parameter must be defined in the **MuRAT input file**. + +## Required Input Parameter + +In the **input configuration file** (e.g., `Murat_input.mlx` or similar), you must add the following line inside the **`Waveform Data`** section: + + +Murat.input.envelopeSmoothTime = 1; + + +## Parameter Description + +* **`Murat.input.envelopeSmoothTime`** + Defines the smoothing time (in seconds) applied when computing the waveform envelope. + +* **Recommended value:** + `1` second + +This parameter is required by `Murat_envelope.m` to correctly smooth the envelope after it is computed from the seismic waveform. + +## Where to Place the Parameter + +Insert the line in the **Waveform Data** section of the input file, together with the other waveform-related parameters. + +Example: + + +%% Waveform Data + +Murat.input.envelopeSmoothTime = 1; + + +## Notes + +* If this parameter is not defined, the envelope computation may fail or produce unexpected results. +* The value can be adjusted depending on the desired smoothing level, but **1 second is the standard value used in most MuRAT workflows**. + diff --git a/Utilities_Matlab/MyUtilities/Murat_changeHdr.m b/Utilities_Matlab/PrePostProcessing/Murat_changeHdr.m similarity index 100% rename from Utilities_Matlab/MyUtilities/Murat_changeHdr.m rename to Utilities_Matlab/PrePostProcessing/Murat_changeHdr.m diff --git a/Utilities_Matlab/MyUtilities/Murat_plotMore.m b/Utilities_Matlab/PrePostProcessing/Murat_plotMore.m similarity index 100% rename from Utilities_Matlab/MyUtilities/Murat_plotMore.m rename to Utilities_Matlab/PrePostProcessing/Murat_plotMore.m diff --git a/Utilities_Matlab/MyUtilities/Murat_test.m b/Utilities_Matlab/PrePostProcessing/Murat_test.m similarity index 100% rename from Utilities_Matlab/MyUtilities/Murat_test.m rename to Utilities_Matlab/PrePostProcessing/Murat_test.m diff --git a/Utilities_Matlab/MyUtilities/Murat_testAll.m b/Utilities_Matlab/PrePostProcessing/Murat_testAll.m similarity index 100% rename from Utilities_Matlab/MyUtilities/Murat_testAll.m rename to Utilities_Matlab/PrePostProcessing/Murat_testAll.m diff --git a/Utilities_Matlab/PrePostProcessing/freq_analysis.m b/Utilities_Matlab/PrePostProcessing/freq_analysis.m new file mode 100644 index 0000000..a7206bc --- /dev/null +++ b/Utilities_Matlab/PrePostProcessing/freq_analysis.m @@ -0,0 +1,184 @@ +function freq_analysis(path_s, path_w) +% +% FREQ_ANALYSIS analyzes the waveform data in sac format returning +% plot of the frequency content and arrival time of P and S. +% It uses STA/LTA to exclude the noise before the analysis. +% The same method is adopted to estimate the coda start which +% is plotted as histogram. +% +% Input parameters: +% path_s: path of the folder for storing the output images +% path_w: path of the folder containing the waveforms +% +% Output parameters: +% Frequency_distribution.png: boxplot of the frequency content for all +% the waveforms +% Auto_Coda_Start.png: histogram plot of the coda start +% (trigger-off values from STA/LTA analysis) +% P_arrival_times.png: histogram plot of the P-waves arrival +% times (difference from the t0) +% S_arrival_times: histogram plot of the S-waves arrival +% times (difference from the t0) +% +% Check_Plots folder: random plot of waveforms +% +disp('Analyzing the frequency content with STA/LTA') + +% List of the sac data +sacpath = fullfile(path_w, '*.sac'); +sacfiles = dir(sacpath); + +% STA/LTA parameters +sta_len = 1.0; +lta_len = 10.0; +thresh_on = 3.0; +thresh_off = 1.2; + +% Spectra parameters +max_freq = 50; +freq_bins = 0:1:max_freq; +n_bins = length(freq_bins) - 1; +to_boxplot = zeros(length(sacfiles), n_bins); +p_arr = zeros(length(sacfiles),1); +s_arr = zeros(length(sacfiles),1); +t_trigger_on = NaN(length(sacfiles),1); +t_coda_start = NaN(length(sacfiles),1); + +% Random plot settings +n_check_plots = 10; +indices_to_plot = randperm(length(sacfiles), min(n_check_plots, length(sacfiles))); +check_dir = fullfile(path_s, 'Check_Plots'); +if ~exist(check_dir, 'dir') + mkdir(check_dir); +end + +% Iteration +for j = 1:length(sacfiles) + % reading data + sacfile = strcat(path_w,sacfiles(j).name); + [~, data, hdr] = fread_sac(sacfile); + fs = 1 / hdr.delta; + p_arr(j) = hdr.a - hdr.o; + s_arr(j) = hdr.t(1) - hdr.o; + data = detrend(data); + + % STA/LTA + cf = data.^2; + n_sta = round(sta_len * fs); + n_lta = round(lta_len * fs); + if length(cf) < n_lta + warning(['Waveform too short for LTA: ' sacfiles(j).name]); + continue; + end + b_sta = ones(1, n_sta) / n_sta; + b_lta = ones(1, n_lta) / n_lta; + sta = filter(b_sta, 1, cf); + lta = filter(b_lta, 1, cf); + lta(lta == 0) = eps; + ratio = sta ./ lta; + ratio(1:n_lta) = 0; + idx_on = find(ratio > thresh_on, 1, 'first'); + if isempty(idx_on) + warning(['No event detected in ', sacfiles(j).name]); + continue; + end + idx_future = find(ratio(idx_on:end) < thresh_off, 1, 'first'); + if isempty(idx_future) + idx_off = length(data); + else + idx_off = idx_on + idx_future - 1; + end + t_on_sec = (idx_on - 1) * hdr.delta; + t_off_sec = (idx_off - 1) * hdr.delta; + t_trigger_on(j) = t_on_sec; + t_coda_start(j) = t_off_sec; + + % random plot + if ismember(j, indices_to_plot) + h_fig = figure('Visible', 'off'); + t_axis = (0:length(data)-1) * hdr.delta; + subplot(2,1,1); + plot(t_axis, data, 'k'); hold on; + title(['Waveform: ' sacfiles(j).name], 'Interpreter', 'none'); + ylabel('Amplitude'); + grid on; + t_start = 0; + xline(hdr.o - t_start, 'b', 'T0', 'LineWidth', 1.5, 'LabelVerticalAlignment','bottom'); + xline(hdr.a - t_start, 'g', 'P (Head)', 'LineWidth', 1.5, 'LabelVerticalAlignment','bottom'); + xline(hdr.t(1) - t_start, 'r', 'S (Head)', 'LineWidth', 1.5, 'LabelVerticalAlignment','bottom'); + xline(t_on_sec, 'b--', 'Trig ON', 'LineWidth', 1.5); + xline(t_off_sec, 'm--', 'Trig OFF', 'LineWidth', 1.5); + legend('Data','T0','P','S','Trig ON','Trig OFF','Location','best'); + subplot(2,1,2); + plot(t_axis, ratio, 'b'); hold on; + yline(thresh_on, 'g--', 'Thresh ON'); + yline(thresh_off, 'r:', 'Thresh OFF'); + title('STA/LTA Ratio'); + xlabel('Time (s)'); + grid on; + saveas(h_fig, fullfile(check_dir, ['Check_' sacfiles(j).name '.png'])); + close(h_fig); + end + + % power spectra + pad = round(1.0 * fs); + i1 = max(1, idx_on - pad); + i2 = min(length(data), idx_off + pad); + data_cut = data(i1:i2); + if length(data_cut) > fs + [pw, fq] = pspectrum(data_cut,fs); + for i = 1:n_bins + idx_in_bin = fq >= freq_bins(i) & fq < freq_bins(i+1); + if any(idx_in_bin) + to_boxplot(j, i) = mean(pw(idx_in_bin)); + else + to_boxplot(j, i) = NaN; + end + end + end +end + +disp('Saving results') +% Plot the frequency content +figure; +freq_centers = freq_bins(1:end-1) + 0.5; +to_boxplot = to_boxplot(~all(isnan(to_boxplot), 2), :); +boxplot(to_boxplot, 'Labels', string(freq_centers), 'Symbol','r.','OutlierSize',2); +xlabel('Frequency (Hz)'); +a = get(gca,'XTickLabel'); +set(gca,'XTickLabel',a,'fontsize',6) +ylabel('Amplitude of the power spectra'); +title('Box plot (1 Hz bin)'); +yscale log +saveas(gcf, [path_s '/Frequency_distribution.png']); + +% Plot the trigger off (coda onset) +t_coda_start = t_coda_start(~isnan(t_coda_start)); +if ~isempty(t_coda_start) + figure; + histogram(t_coda_start, 20); + xlabel('Time (s from start of file)'); + ylabel('Number of events'); + title(['Automatic Coda Start (LTA=' num2str(lta_len) 's, Thr=' num2str(thresh_off) ')']); + grid on; + saveas(gcf, fullfile(path_s, 'Auto_Coda_Start.png')); +end + +% Plot the P arrival time +figure; +histogram(p_arr) +xlabel('Arrival time (s from t0)'); +ylabel('Number of events'); +title('Arrival time of P wave'); +grid on; +saveas(gcf, [path_s '/P_arrival_times.png']); + +% Plot the S arrival time +figure; +histogram(s_arr) +xlabel('Arrival time (s from t0)'); +ylabel('Number of events'); +title('Arrival time of S wave'); +grid on; +saveas(gcf, [path_s '/S_arrival_times.png']); +end \ No newline at end of file diff --git a/Utilities_Matlab/PrePostProcessing/hitmap.m b/Utilities_Matlab/PrePostProcessing/hitmap.m new file mode 100644 index 0000000..29e03bd --- /dev/null +++ b/Utilities_Matlab/PrePostProcessing/hitmap.m @@ -0,0 +1,96 @@ +function hitmap(Murat, x0, y0, x1, y1, Ns) +% +% HITMAP produces horizontal and vertical cross section displaying the +% seismic ray count as hitmap. 3 default sections in the +% model are built (N-S, W-E, and horizontal). Than, +% starting and ending coordinates of a custom oblique +% cross sections can be added. +% +% Input parameters: +% Murat: Data structure as built by Murat +% x0: Longitude of the custom starting point for the oblique +% cross section +% y0: Latitude of the custom starting point for the oblique +% cross section +% x1: Longitude of the custom ending point for the oblique +% cross section +% y1: Latitude of the custom ending point for the oblique +% cross section +% Ns: Number of points along the custom oblique section +% +% Output parameters: +% Horizontal_rays_hitmap.png: Horizontal cross section of the total ray +% count as hitmap +% WE_rays_hitmap.png: Vertical W-E cross section of the total +% ray count as hitmap +% SN_rays_hitmap.png: Vertical S-N cross section of the total +% ray count as hitmap +% Section_rays_hitmap: Oblique vertical cross section of the total +% ray count as hitmap +% + +% Dafault values for starting and ending points +arguments + Murat + x0 = NaN + y0 = NaN + x1 = NaN + y1 = NaN + Ns = 100 +end + +disp('Building horizontal and vertical hitmaps') + +% Get the data from Murat structure +try + rayCrossing = Murat.rays.rayCrossing'; +catch + rayCrossing = Murat.data.rayCrossing'; +end +grid = Murat.input.DDcoordinates; +x = grid(:,1); +y = grid(:,2); +z = grid(:,3); +tbl = table(x,y,z,rayCrossing); + +% Build default hitmaps +figure; +h = heatmap(tbl,'x','y','ColorVariable','rayCrossing','Colormap', hot); +h.NodeChildren(3).YDir='normal'; +saveas(gcf,'Horizontal_rays_hitmap.png'); +figure; +vx = heatmap(tbl,'x','z','ColorVariable','rayCrossing','Colormap', hot); +vx.NodeChildren(3).YDir='normal'; +saveas(gcf,'WE_rays_hitmap.png'); +figure; +vy = heatmap(tbl,'y','z','ColorVariable','rayCrossing','Colormap', hot); +vy.NodeChildren(3).YDir='normal'; +saveas(gcf,'SN_rays_hitmap.png'); + +if ~isnan(x0) && ~isnan(y0) && ~isnan(x1) && ~isnan(y1) + % Build the new oblique vertical section + dx = x1 - x0; + dy = y1 - y0; + L = sqrt(dx^2 + dy^2); + if L > 0 + s = linspace(0, L, Ns); + zq = unique(z); + [Xs, Zs] = meshgrid(s, zq); + Xs = x0 + (x1 - x0) * (Xs / L); + Ys = y0 + (y1 - y0) * (Xs - x0) / (x1 - x0); + + % Value interpolation and plot + F = scatteredInterpolant(x,y,z,rayCrossing, 'natural', 'none'); + Vs = F(Xs, Ys, Zs); + figure; + imagesc(s, zq, Vs); + set(gca, 'YDir', 'normal','Colormap', hot); + xlabel('Distance along section'); + ylabel('Elevation (m)'); + colorbar; + saveas(gcf,'Section_rays_hitmap.png'); + else + disp("Error: section length is 0") + end +end + diff --git a/_config.yml b/_config.yml index dbe923a..356d5b9 100644 --- a/_config.yml +++ b/_config.yml @@ -1,3 +1,3 @@ theme: jekyll-theme-dinky title: Multi-Resolution Attenuation Tomography -description: The open-assess solution to seismic attenuation imaging! \ No newline at end of file +description: The open-access solution to seismic attenuation imaging! \ No newline at end of file diff --git a/bin/Murat_DDcoordinates.m b/bin/Murat_DDcoordinates.m index 6a9a697..05a4562 100644 --- a/bin/Murat_DDcoordinates.m +++ b/bin/Murat_DDcoordinates.m @@ -17,40 +17,15 @@ % order %% -% Calculating increments -lat_incr = (ending(1)-origin(1))/(nLat-1); -lon_incr = (ending(2)-origin(2))/(nLong-1); -dep_incr = (ending(3)-origin(3))/(nzc-1); -% Creating vectors for x,y,z -xLong = zeros(nLong,1); -yLat = zeros(nLat,1); -zDep = zeros(nzc,1); -lon=origin(2); -for i=1:nLong - xLong(i) = lon; - lon = lon+lon_incr; -end +% coordinate vectors +yLat = linspace(origin(1), ending(1), nLat); % latitude (j) +xLong = linspace(origin(2), ending(2), nLong); % longitude (i) +zDep = linspace(origin(3), ending(3), nzc); % depth (k) -lat = origin(1); -for i=1:nLat - yLat(i) = lat; - lat = lat+lat_incr; -end +% produce grids with same indexing as original nested loops: +[X, Y, Z] = ndgrid(xLong, yLat, zDep); -dep = origin(3); -for i=1:nzc - zDep(i) = dep; - dep = dep+dep_incr; -end % Generating matrix of coordinates -DD_coord = zeros(nLong*nLat*nzc,3); -index = 0; -for i=1:nLong - for j=1:nLat - for k=1:nzc - index = index+1; - DD_coord(index,1:3) = [xLong(i) yLat(j) zDep(k)]; - end - end -end +DD_coord = [X(:), Y(:), Z(:)]; + end \ No newline at end of file diff --git a/bin/Murat_Qc.m b/bin/Murat_Qc.m index 75e8766..13877bc 100644 --- a/bin/Murat_Qc.m +++ b/bin/Murat_Qc.m @@ -1,136 +1,122 @@ -function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,sp_i,... - cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement) -% function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,sp_i,... -% cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement) +function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,... + sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement) +% function [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,... +% sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasurement) % % MEASURES Qc and its uncertainty. % % Input parameters: % cf: central frequency -% sped: spectral decay -% sp_i: envelopes +% sp_i: filtered envelope % cursorCodaStart_i: start of the coda on trace % cursorEndStart_i: end of the coda on trace % tCoda_i: start of the coda in seconds % srate_i: sampling rate % QcMeasurement: decides if Linearized or NonLinear solutions +% % Output parameters: % inverseQc_i: inverse coda attenuation factor % uncertaintyQc_i: uncertainty on inverse coda attenuation factor -lcf = length(cf); -inverseQc_i = zeros(lcf,1); -uncertaintyQc_i = zeros(lcf,1); +lcf = numel(cf); +inverseQc_i = zeros(lcf,1); +uncertaintyQc_i = zeros(lcf,1); +srate_i = round(srate_i); +si = 1/srate_i; for i = 1:lcf - - envelopeC =... - sp_i(cursorCodaStart_i:cursorCodaEnd_i,i); - lEnvelopeC = length(envelopeC); - - cf_i = cf(i); - - if isempty(envelopeC) - - inverseQc_i(i) = 0; - uncertaintyQc_i(i) = 0; - - else - - if isequal(QcMeasurement,'Linearized') - - lapseT =... - (tCoda_i+1/srate_i:1/srate_i:tCoda_i+lEnvelopeC/srate_i)'; - - [linearFit, uncertaintyFit] =... - estimatesLinear(lapseT,envelopeC,lEnvelopeC,sped,cf_i); + envelopeC = sp_i(cursorCodaStart_i:cursorCodaEnd_i,i); + lEnvelopeC = numel(envelopeC); + cf_i = cf(i); + + switch QcMeasurement + + case 'Linearized' - if linearFit(1)<0 - inverseQc_i(i) = -linearFit(1); - uncertaintyQc_i(i) = abs(uncertaintyFit(1,2)); + lapseT = (tCoda_i+si:si:tCoda_i+lEnvelopeC*si)'; + + edgeC = floor(0.05*lEnvelopeC); + lapseTime = lapseT(edgeC:end-edgeC); + spcm1 = envelopeC(edgeC:end-edgeC); + + weigthEnergy = spcm1.*lapseTime.^1.5; + logWEnergy = log(weigthEnergy)/2/pi/cf_i; + + p = polyfit(lapseTime,logWEnergy,1); + R = corrcoef([lapseTime,logWEnergy]); + + if p(1)<0 + inverseQc_i(i) = -p(1); + RZZ_k = abs(R(1,2)); + w = 1 ./ RZZ_k; + %w = (w - min(w(:))) ./ (max(w(:)) - min(w(:))); + uncertaintyQc_i(i) = w; + else - inverseQc_i(i) = 0; - uncertaintyQc_i(i) = 0; + inverseQc_i(i) = 0; + uncertaintyQc_i(i) = 0; end - - elseif isequal(QcMeasurement,'NonLinear') - QValues = 0:10^-5:10^-1; - - lWindow =... - round((cursorCodaEnd_i-cursorCodaStart_i)/srate_i); - lapseT =... - (tCoda_i+0.5:tCoda_i+lWindow-0.5)'; - - [nonLinearFit, uncertaintyFit] = estimatesNonLinear(lapseT,... - envelopeC,QValues,sped,lWindow,cf_i,srate_i); - - inverseQc_i(i) = nonLinearFit; - uncertaintyQc_i(i) = uncertaintyFit; - - else - - error('Unknown strategy to calculate Qc'); - - end - - end - -end -uncertaintyQc_i(inverseQc_i==0) = 0; + case 'NonLinear' + QValues = 10^-5:10^-5:10^-1; + lWindow = round((cursorCodaEnd_i-cursorCodaStart_i)*si); -end -%% -% Calculations in the linarized case. -function [linearFit, uncertaintyFit] =... - estimatesLinear(lapseT,envelopeC,lEnvelopeC,sped,cf_i) + % returns srate_i-by-nCols matrix + src = envelopeC(:); + nCols = min(lWindow, floor(numel(src) / srate_i));% n full col. + if nCols == 0 + blk = []; % no full blocks available + else + blk = reshape(src(1 : nCols * srate_i), srate_i, nCols); + end + + dObs = mean(blk)'; -%Only evaluate central time series -edgeC = floor(0.05*lEnvelopeC); -lapseTime = lapseT(edgeC:end-edgeC); -spcm1 = envelopeC(edgeC:end-edgeC); + % normalized observed (exclude last sample, normalize by last) + dObserved = dObs(1:end-1) / dObs(end); -weigthEnergy = spcm1.*lapseTime.^sped; -logWEnergy = log(weigthEnergy)/2/pi/cf_i; + % lapse times for blocks + lapseT_blk = (tCoda_i + 0.5 : tCoda_i + size(blk,2) - 0.5)'; -linearFit =... - polyfit(lapseTime,logWEnergy,1); -uncertaintyFit =... - corrcoef([lapseTime,logWEnergy]); + % prepare predicted model matrix for all QValues at once + L = lapseT_blk(:); + const = 2 * pi * cf_i; -end + % compute matrix of exponents: (lapseT * QValues') is m x nQ + expMat = exp(- const * (L * QValues(:)')); + dPreMat = (L .^ -1.5) .* expMat; -%% -%% -% Calculations in the non-linar (grid-search) case. + % normalize each column by its last element, then drop last row + den = dPreMat(end, :); + dPredicted = dPreMat(1:end-1, :) ./ den; -function [nonLinearFit, uncertaintyFit] =... - estimatesNonLinear(lapseT,envelopeC,QValues,sped,lWindow,cf_i,srate_i) + % compute L1 misfit for each QValue + Evec = sum(abs(dObserved - dPredicted), 1); -lQValues = length(QValues); + % pick best fit and compute uncertainty + [Emin, idx] = min(Evec); + nonLinearFit = QValues(idx); + uncertaintyFit = abs(log(Emin / mean(lapseT_blk)^1.5) /... + (2 * pi * cf_i * mean(lapseT_blk))); + + if nonLinearFit == min(QValues) + inverseQc_i(i) = 0; + uncertaintyQc_i(i) = 0; + else + inverseQc_i(i) = nonLinearFit; + uncertaintyQc_i(i) = uncertaintyFit; + end -dObs = zeros(lWindow,1); + otherwise -for k = 1:lWindow - ntm = (k-1)*srate_i + 1:k*srate_i; - dObs(k,1) = mean(envelopeC(floor(ntm))); -end - -dObserved = dObs(1:end-1)/dObs(end); + error('Unknown strategy to calculate Qc'); + end -E = zeros(lQValues,1); -for n = 1:lQValues - dPre =... - lapseT.^(-sped).*exp(-2*pi*cf_i.*lapseT*QValues(n)); - - dPredicted = dPre(1:end-1)/dPre(end); - E(n,1) =... - sum(abs(dObserved-dPredicted)); end -[Emin, indexEmin] = min(E); -nonLinearFit = QValues(indexEmin); -uncertaintyFit = 1/Emin; +% force zero uncertainty where inverseQc is zero +uncertaintyQc_i(inverseQc_i <= 0) = 0; +inverseQc_i(uncertaintyQc_i<0) = 0; end diff --git a/bin/Murat_bending.m b/bin/Murat_bending.m index e598153..9250984 100644 --- a/bin/Murat_bending.m +++ b/bin/Murat_bending.m @@ -1,6 +1,7 @@ function [xtemp,ytemp,ztemp,v] =... Murat_bending(xtemp,ytemp,ztemp,gridD,v,pvel) -% function [xtemp,ytemp,ztemp,v]= Murat_bending(xtemp,ytemp,ztemp,gridD,v,pvel) +% function [xtemp,ytemp,ztemp,v]=... +% Murat_bending(xtemp,ytemp,ztemp,gridD,v,pvel) % % BENDS the initial segment along the normal to the % ray path tangent at each point by an optimal distance r. @@ -20,65 +21,72 @@ % ztemp: z after bending % v: final velocity of the ray -xfac = 1; -n = length(xtemp); +xfac = 1; +n = length(xtemp); for k=2:(n-1) - kk = k-1; - kkk = k+1; + kk = k-1; + kkk = k+1; % compute the normal direction of maximum gradient of velocity - dx = xtemp(kkk)-xtemp(kk); - dy = ytemp(kkk)-ytemp(kk); - dz = ztemp(kkk)-ztemp(kk); - dn = dx^2+dy^2+dz^2; - ddn = sqrt(dn); - rdx = dx/ddn; - rdy = dy/ddn; - rdz = dz/ddn; - xk = 0.5*dx+xtemp(kk); - yk = 0.5*dy+ytemp(kk); - zk = 0.5*dz+ztemp(kk); + % segment vector and its length + dx = xtemp(kkk) - xtemp(kk); + dy = ytemp(kkk) - ytemp(kk); + dz = ztemp(kkk) - ztemp(kk); + dn = dx*dx + dy*dy + dz*dz; + ddn = sqrt(dn); + inv_ddn = 1 / ddn; + rdx = dx * inv_ddn; + rdy = dy * inv_ddn; + rdz = dz * inv_ddn; + + % midpoint + xk = xtemp(kk) + 0.5*dx; + yk = ytemp(kk) + 0.5*dy; + zk = ztemp(kk) + 0.5*dz; + + % velocity and gradient (vel dicity) at midpoint + vk = Murat_velocity(xk,yk,zk,gridD,pvel); + [vx,vy,vz] = Murat_veldicity(xk,yk,zk,gridD,pvel); - vk = Murat_velocity(xk,yk,zk,gridD,pvel); - [vx,vy,vz] = Murat_veldicity(xk,yk,zk,gridD,pvel); - vrd = vx*rdx+vy*rdy+vz*rdz; - rvx = vx-vrd*rdx; - rvy = vy-vrd*rdy; - rvz = vz-vrd*rdz; - rvs = sqrt(rvx*rvx+rvy*rvy+rvz*rvz); + % gradient component perpendicular to tangent + vrd = vx*rdx + vy*rdy + vz*rdz; + rvx = vx - vrd*rdx; + rvy = vy - vrd*rdy; + rvz = vz - vrd*rdz; + rvs = sqrt(rvx*rvx + rvy*rvy + rvz*rvz); % check for zero gradient if (rvs == 0) % zero gradient - straight path - xtemp(k) = xk; - ytemp(k) = yk; - ztemp(k) = zk; - vk = Murat_velocity(xk,yk,zk,gridD,pvel); - v(k) = vk; + xtemp(k) = xk; + ytemp(k) = yk; + ztemp(k) = zk; + v(k) = vk; else - rvx = rvx/rvs; - rvy = rvy/rvs; - rvz = rvz/rvs; + inv_rvs = 1 / rvs; + rvx = rvx * inv_rvs; + rvy = rvy * inv_rvs; + rvz = rvz * inv_rvs; % compute the optimal distance r - c = (1/v(kkk)+1/v(kk))/2; - c1 = (c*vk+1)/(4*c*rvs); - rtemp = -c1+sqrt(c1^2+dn/(8*c*vk)); + c = (1/v(kkk) + 1/v(kk))*0.5; + c1 = (c*vk+1) / (4*c*rvs); + rtemp = -c1 + sqrt(c1^2 + dn/(8*c*vk)); % compute the new points and distance of perturbations - xxk = xk+rvx*rtemp; - yyk = yk+rvy*rtemp; - zzk = zk+rvz*rtemp; + xxk = xk + rvx*rtemp; + yyk = yk + rvy*rtemp; + zzk = zk + rvz*rtemp; % convergence enhancement - xxk = xfac*(xxk-xtemp(k))+xtemp(k); - yyk = xfac*(yyk-ytemp(k))+ytemp(k); - zzk = xfac*(zzk-ztemp(k))+ztemp(k); + xxk = xfac * (xxk-xtemp(k)) + xtemp(k); + yyk = xfac * (yyk-ytemp(k)) + ytemp(k); + zzk = xfac * (zzk-ztemp(k)) + ztemp(k); - xtemp(k) = xxk; - ytemp(k) = yyk; - ztemp(k) = zzk; - v(k) = Murat_velocity(xxk,yyk,zzk,gridD,pvel); + xtemp(k) = xxk; + ytemp(k) = yyk; + ztemp(k) = zzk; + v(k) = Murat_velocity(xxk,yyk,zzk,gridD,pvel); end -end +end \ No newline at end of file diff --git a/bin/Murat_body.m b/bin/Murat_body.m index d3f4751..b2698b6 100644 --- a/bin/Murat_body.m +++ b/bin/Murat_body.m @@ -1,8 +1,9 @@ -function [energyRatioBodyCoda_i,energyRatioCodaNoise_i]... - = Murat_body(bodyWindow,startNoise,srate_i,... - sp_i,cursorPick_i,cursorCodaStart_i,cursorCodaEnd_i) +function [energyRatioBodyCoda_i,energyRatioCodaNoise_i] =... + Murat_body(bodyWindow,startNoise,srate_i,sp_i,... + cursorPick_i,cursorCodaStart_i,cursorCodaEnd_i) % function [energyRatioBodyCoda_i,energyRatioCodaNoise_i] =... -% Murat_body(Murat,srate_i,sp_i,cursorPick_i,cursorCodaStart_i,cursorCodaEnd_i) +% Murat_body(bodyWindow,startNoise,srate_i,cursorPick_i,... +% cursorCodaStart_i,cursorCodaEnd_i) % % CREATES the body-to-coda energy ratios and uncertainties necessary for % the CN method. @@ -21,25 +22,29 @@ % energyRatioCodaNoise_i: energy ratio between coda waves and noise % Redefine windows with sampling for noise and direct waves -int = bodyWindow*srate_i; -lc = length(cursorCodaStart_i:cursorCodaEnd_i); -cursor0 = floor(startNoise*srate_i); -cursor0_1 = floor(cursor0 + int); -cursor2 = floor(cursorPick_i + int-1); +intSamples = floor(bodyWindow*srate_i); +cursorNoise0 = floor(startNoise*srate_i); +cursor0Noise1 = cursorNoise0 + intSamples; +cursorPS1 = floor(cursorPick_i + intSamples-1); +lengthSamples = length(cursorCodaStart_i:cursorCodaEnd_i); -% Direct wave normalised by window length -spamp =... - trapz(sp_i(cursorPick_i:cursor2,:))/int; +% Extract windows (vectorized) +spPS = sp_i(cursorPick_i:cursorPS1,:); +spNoise = sp_i(cursorNoise0:cursor0Noise1,:); +spCoda = sp_i(cursorCodaStart_i:cursorCodaEnd_i,:); -% Noise pre-pick normalised by window length -spampn =... - trapz(sp_i(cursor0:cursor0_1,:))/int; +% Integrate using trapz with correct spacing +dt = 1 / srate_i; +spampPS = trapz((0:dt:dt*(size(spPS,1)-1)), spPS); +spampNoise = trapz((0:dt:dt*(size(spNoise,1)-1)), spNoise); -% Coda normalised by window length -spampc =... - trapz(sp_i(cursorCodaStart_i:cursorCodaEnd_i,:))/lc; -energyRatioBodyCoda_i = spamp./spampc; -energyRatioCodaNoise_i = spampc./spampn; +% Normalize coda by relative window length +differentWindows = lengthSamples/intSamples; +spampCoda = ... + trapz((0:dt:dt*(size(spCoda,1)-1)), spCoda)./ differentWindows; + +energyRatioBodyCoda_i = spampPS ./ spampCoda; +energyRatioCodaNoise_i = spampCoda ./ spampNoise; end \ No newline at end of file diff --git a/bin/Murat_checks.m b/bin/Murat_checks.m index 124f004..1b3b2b5 100644 --- a/bin/Murat_checks.m +++ b/bin/Murat_checks.m @@ -1,151 +1,157 @@ % ADDITIONAL input variables that are not set by the user. -function Murat = Murat_checks(Murat) - -dataDirectory = [Murat.input.dataDirectory]; -FPath = './'; -FLabel = Murat.input.label; -PTime = ['SAChdr.times.' Murat.input.PTime]; -PorS = Murat.input.POrS; - -origin = Murat.input.origin; -ending = Murat.input.end; -nLat = Murat.input.gridLat; -nLong = Murat.input.gridLong; -nzc = Murat.input.gridZ; -availableVelocity = Murat.input.availableVelocity; -velocityModel = ['velocity_models/',Murat.input.namev]; +function Murat = Murat_checks(Murat) + +dataDirectory = Murat.input.dataDirectory; +FLabel = Murat.input.label; +FPath = './'; +velocityModel = fullfile('velocity_models', Murat.input.namev); + +% time fields +PTime = ['SAChdr.times.' Murat.input.PTime]; +PorS = Murat.input.POrS; +origin = Murat.input.origin; +ending = Murat.input.end; +nLat = Murat.input.gridLat; +nLong = Murat.input.gridLong; +nzc = Murat.input.gridZ; +availableVelocity = Murat.input.availableVelocity; if isempty(Murat.input.originTime) - originTime = 'SAChdr.times.o'; + originTime = 'SAChdr.times.o'; else - originTime = ['SAChdr.times.' Murat.input.originTime]; + originTime = ['SAChdr.times.' Murat.input.originTime]; end if isempty(Murat.input.STime) - STime = 'SAChdr.times.t0'; + STime = 'SAChdr.times.t0'; else - STime = ['SAChdr.times.' Murat.input.STime]; + STime = ['SAChdr.times.' Murat.input.STime]; end -Murat.input.originTime = originTime; -Murat.input.PTime = PTime; -Murat.input.STime = STime; +Murat.input.originTime = originTime; +Murat.input.PTime = PTime; +Murat.input.STime = STime; - -if exist('./temp','dir')==7 - delete('./temp/*') +if isfolder('./temp') + delete(fullfile('./temp','*')) else mkdir('./temp') end -%% Creating folders to store results -if exist(FLabel,'dir')==7 +%% Create folder tree in a loop to avoid repeated mkdir calls +subdirs = { '', 'Rays', 'Kernels', 'Tests', 'Tests/PeakDelay', ... + 'Tests/Qc' 'Tests/Q', 'Tests/LCurve', 'Tests/Clustering', 'Results',... + 'Results/PeakDelay', 'Results/Qc', 'Results/Q','Results/Parameter',... + 'Checkerboard', 'Checkerboard/Qc', 'Checkerboard/PD' ... + 'Checkerboard/Q', 'Spike', 'Spike/Qc', 'Spike/Q', 'TXT' }; + +if isfolder(FLabel) rmdir(FLabel,'s') end - -mkdir(FLabel) -mkdir([FLabel,'/Rays']) -mkdir([FLabel,'/Kernels']) -mkdir([FLabel,'/Tests']) -mkdir([FLabel,'/Tests/PeakDelay']) -mkdir([FLabel,'/Tests/Qc']) -mkdir([FLabel,'/Tests/Q']) -mkdir([FLabel,'/Tests/LCurve']) -mkdir([FLabel,'/Results']) -mkdir([FLabel,'/Results/PeakDelay']) -mkdir([FLabel,'/Results/Qc']) -mkdir([FLabel,'/Results/Q']) -mkdir([FLabel,'/Results/Parameter']) -mkdir([FLabel,'/Checkerboard']) -mkdir([FLabel,'/Checkerboard/Qc']) -mkdir([FLabel,'/Checkerboard/Q']) -mkdir([FLabel,'/Spike']) -mkdir([FLabel,'/Spike/Qc']) -mkdir([FLabel,'/Spike/Q']) -mkdir([FLabel,'/TXT']) +for i = 1:numel(subdirs) + mkdir(fullfile(FLabel, subdirs{i})) +end %% Checking data -[Murat.input.listSac,~] = createsList([dataDirectory '/*.sac']); -[header,flag] =... +[Murat.input.listSac,~] = createsList([dataDirectory '/*.sac']); +[header,flag,sacHeader] =... Murat_testData(dataDirectory,originTime,PTime,STime); -mLat =... - [min(cell2mat(header{:,5})) max(cell2mat(header{:,5}))]; -mLon =... - [min(cell2mat(header{:,6})) max(cell2mat(header{:,6}))]; -if isequal(flag,1) - warning('Missing origin times.') -end +mLat = [min(cell2mat(header{:,5})) max(cell2mat(header{:,5}))]; +mLon = [min(cell2mat(header{:,6})) max(cell2mat(header{:,6}))]; + +if flag == 1, warning('Missing origin times.'); end +if flag == 2, warning('Missing S-wave times.'); end -if isequal(flag,2) - warning('Missing S-wave times.') -end %% VELOCITY MODELS: ORIGINAL, INVERSION, and PROPAGATION % Save x,y,z in degrees switching as longitude comes second % Find distance and azimuth to change in meters -modvOriginal = load(velocityModel); - -wgs84 = wgs84Ellipsoid("m"); -[d,az] =... +wgs84 = wgs84Ellipsoid("m"); +[d,az] =... distance(origin(1),origin(2),ending(1),ending(2),wgs84); -dist_x = d*sin(az*2*pi/360); -dist_y = d*cos(az*2*pi/360); + +% convert az from degrees to radians for sin/cos +azr = deg2rad(az); +dist_x = d.*sin(azr); +dist_y = d.*cos(azr); % Coordinates of inversion points in meters -xM = linspace(0,dist_x,nLong)'; -yM = linspace(0,dist_y,nLat)'; -zM = linspace(origin(3),ending(3),nzc)'; -modvXYZ = Murat_unfoldXYZ(xM,yM,zM); +xM = linspace(0,dist_x,nLong).'; +yM = linspace(0,dist_y,nLat).'; +zM = linspace(origin(3),ending(3),nzc).'; +modvXYZ = Murat_unfoldXYZ(xM,yM,zM); -Murat.input.x = linspace(origin(2),ending(2),nLong)'; -Murat.input.y = linspace(origin(1),ending(1),nLat)'; -Murat.input.z = linspace(origin(3),ending(3),nzc)'; -Murat.input.gridStepX = xM(2)-xM(1); -Murat.input.gridStepY = yM(2)-yM(1); +Murat.input.x = linspace(origin(2),ending(2),nLong).'; +Murat.input.y = linspace(origin(1),ending(1),nLat).'; +Murat.input.z = linspace(origin(3),ending(3),nzc).'; +Murat.input.gridStepX = xM(2) - xM(1); +Murat.input.gridStepY = yM(2) - yM(1); if availableVelocity == 0 + + qLat = mean([origin(1),ending(1)]); + qLon = mean([origin(2),ending(2)]); + - gridPropagation.x = xM'; - gridPropagation.y = yM'; - gridPropagation.z = zM'; + gridPropagation.x = xM'; + gridPropagation.y = yM'; + gridPropagation.z = zM'; - [modv,pvel,modvPlot] =... + if isequal(Murat.input.namev,"LITHO1.0.nc") + modvOriginal = Murat_readLithos1(velocityModel,qLat,qLon); + else + modvOriginal = load(velocityModel); + end + + [modv,pvel,modvPlot]=... Murat_modv1D(modvXYZ,modvOriginal,PorS,origin); - Murat.input.modv = modv; - Murat.input.modvp = modv; - Murat.input.modvPlot = modvPlot; + Murat.input.modv = modv; + Murat.input.modvp = modv; + Murat.input.modvPlot= modvPlot; elseif availableVelocity == 1 - + modvOriginal = load(velocityModel); + [modvP,pvel,modvEqS,modvPlo]= Murat_modv3D(FPath,FLabel,... modvOriginal,origin,mLat,mLon,nLat,nLong,nzc); - gridPropagation.x = unique(modvP(:,1)); - gridPropagation.y = unique(modvP(:,2)); - gridPropagation.z = sort(unique(modvP(:,3)),'descend'); - - Murat.input.modv = modvEqS; - Murat.input.modvp = modvP; - Murat.input.modvPlot = modvPlo; + gridPropagation.x = unique(modvP(:,1)); + gridPropagation.y = unique(modvP(:,2)); + gridPropagation.z = sort(unique(modvP(:,3)),'descend'); + Murat.input.modv = modvEqS; + Murat.input.modvp = modvP; + Murat.input.modvPlot= modvPlo; end Murat.input.gridPropagation = gridPropagation; Murat.input.pvel = pvel; Murat.input.header = header; -DD_coord = Murat_DDcoordinates(origin,ending,nLat,nLong,nzc); -Murat.input.DDcoordinates = DD_coord; -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function [listWithFolder,listNoFolder]... - = createsList(directory) -% CREATES a list of visible files in a folder, outputs both with and -% without folder - -list = dir(directory); -list = list(~startsWith({list.name}, '.')); - -listWithFolder = fullfile({list.folder},{list.name})'; -listNoFolder = {list.name}'; +Murat.input.DDcoordinates = Murat_DDcoordinates(origin,ending,... + nLat,nLong,nzc); +Murat.input.sacHeader = sacHeader; +outDirTXT =... + fullfile(FLabel,'Tests','DataHeaders.xlsx'); +writetable(header,outDirTXT); +end + +%% Local helper (optimized) +function [listWithFolder, listNoFolder] = createsList(directory) +d = dir(directory); +if isempty(d) + listWithFolder = {}; + listNoFolder = {}; + return +end +% remove hidden files (names starting with '.') +names = {d.name}.'; +folders = {d.folder}.'; +mask = ~startsWith(names, '.'); +names = names(mask); +folders = folders(mask); +listWithFolder = fullfile(folders, names); +listNoFolder = names; +end \ No newline at end of file diff --git a/bin/Murat_codaCheck.m b/bin/Murat_codaCheck.m index bc3778b..3b009e9 100644 --- a/bin/Murat_codaCheck.m +++ b/bin/Murat_codaCheck.m @@ -26,38 +26,29 @@ % cursorCodaEnd_i: coda end time after check on trace %Define envelope duration -t00 = tempis(1); -lengthTempis = length(tempis); +t00 = tempis(1); +nSamples = numel(tempis); -% Method peak is generally valid for active seismics -if isequal(peakDelayMethod,'Peak') - - tCoda_i =... - (pktime_i-originTime_i)+peakDelay_i; - -% Method constant is the most used in small local tomography -elseif isequal(peakDelayMethod,'Constant') - - tCoda_i = tCm; - -% Method travel is the standard at the regional scale -elseif isequal(peakDelayMethod,'Travel') - - tCoda_i =... - originTime_i+ tCm*(pktime_i-originTime_i); +% compute reference offsets once +pk_rel = pktime_i - originTime_i; +% Method peak is generally valid for active seismics +switch peakDelayMethod + case 'Peak' + tCoda_i = pk_rel + peakDelay_i; + case 'Constant' + tCoda_i = tCm; + case 'Travel' + tCoda_i = originTime_i + tCm * pk_rel; + otherwise + error("Unknown peak-delay method") end % Define the indexes along the seismogram -cursorCodaStart_i =... - floor((originTime_i - t00 + tCoda_i) * srate_i - 1); -cursorCodaEnd_i =... - floor(cursorCodaStart_i + tWm * srate_i - 1); +cursorCodaStart_i = floor((tCoda_i + originTime_i - t00) * srate_i -1); -% Some seismograms are not long enough so you consider a shorter window -if cursorCodaEnd_i > lengthTempis - cursorCodaEnd_i = lengthTempis; -end +endSample0 = cursorCodaStart_i + floor(tWm * srate_i - 1); +cursorCodaEnd_i = min(nSamples, endSample0); end diff --git a/bin/Murat_codaMatrix.m b/bin/Murat_codaMatrix.m index 88d3571..506355c 100644 --- a/bin/Murat_codaMatrix.m +++ b/bin/Murat_codaMatrix.m @@ -1,7 +1,7 @@ -function AQc_i =... - Murat_codaMatrix(modvQc,K_grid,r_grid,flag,origin,sections) -% function AQc_i =... -% Murat_codaMatrix(modvQc,origin,sections,flag,K_grid,r_grid) +function AQc_i = Murat_codaMatrix(modvQc,K_grid_start,r_grid_start,... + K_grid_end,r_grid_end,flag,origin,sections,FName,pathFolder) +% function AQc_i = Murat_codaMatrix(modvQc,K_grid_start,r_grid_start,... +% K_grid_end,r_grid_end,flag,origin,sections) % % CREATES the coda attenuation inversion matrix and plots the corresponding % kernels @@ -10,7 +10,7 @@ % modvQc: velocity model for grid % K_grid: kernel from Murat_kernels % r_grid: grid from Murat_kernels -% flag: if turned to 1 creates the figure +% flag: if 1 creates the figure for first couple % origin: origin of the grid % sections: sections of the figure % @@ -18,85 +18,109 @@ % AQc_i: coda inversion matrix % Nodes of the kernel model space -xK = unique(r_grid(:,1)); -yK = unique(r_grid(:,2)); -zK = sort(unique(r_grid(:,3)),'descend'); +xKStart = unique(r_grid_start(:,1)); +yKStart = unique(r_grid_start(:,2)); +zKStart = sort(unique(r_grid_start(:,3)),'descend'); +[XkS,YkS,ZkS,KStart]= Murat_fold(xKStart,yKStart,zKStart,K_grid_start); -[Xk,Yk,Zk,K] = Murat_fold(xK,yK,zK,K_grid); +xKEnd = unique(r_grid_end(:,1)); +yKEnd = unique(r_grid_end(:,2)); +zKEnd = sort(unique(r_grid_end(:,3)),'descend'); +[XkE,YkE,ZkE,KEnd] = Murat_fold(xKEnd,yKEnd,zKEnd,K_grid_end); % Interpolated axes for inversion model -x = unique(modvQc(:,1)); -y = unique(modvQc(:,2)); -z = sort(unique(modvQc(:,3)),'descend'); +x = unique(modvQc(:,1)); +y = unique(modvQc(:,2)); +z = sort(unique(modvQc(:,3)),'descend'); +[X,Y,Z,~] = Murat_fold(x,y,z); -% Interpolation sets everything in the right place -[X,Y,Z,~] = Murat_fold(x,y,z); +% Kernels in inversion grid space +mKS = interp3(XkS,YkS,ZkS,KStart,X,Y,Z); +mKE = interp3(XkE,YkE,ZkE,KEnd,X,Y,Z); -% Kernel in inversion grid space -mK = interp3(Xk,Yk,Zk,K,X,Y,Z); +% Replace NaNs and zeros with a tiny positive floor; if entire grid empty, +% create a single-point kernel at the nearest grid node to the max original +tiny = realmin; % machine smallest positive +fixKernel = @(m, K_grid, r_grid, xg, yg, zg) ... + fixKernelLocal(m, K_grid, r_grid, xg, yg, zg, tiny); -%In case limits outside of the grid interpolate better -if find(isnan(mK)) - mK(isnan(mK)) = 10^-100; - mK(mK == 0) = 10^-100; - if isempty(find(mK, 1)) - - mod_K = Murat_unfoldXYZ(x,y,z); - mod_K(:,4) = 0; - [~,maxK] = max(K_grid); - rmax = r_grid(maxK,:); - [~,min_K] = min(sqrt((mod_K(:,1)-rmax(1)).^2 ... - + (mod_K(:,2)-rmax(2)).^2 + (mod_K(:,3)-rmax(3)).^2)); - mod_K(min_K,4) = 1; - [~,~,~,mK] = Murat_fold(x,y,z,mod_K(:,4)); - - end +if any(isnan(mKS(:))) || all(mKS(:) == 0) + mKS = fixKernel(mKS,K_grid_start,r_grid_start,x,y,z); +end +if any(isnan(mKE(:))) || all(mKE(:) == 0) + mKE = fixKernel(mKE,K_grid_end,r_grid_end,x,y,z); end +mK = mKE-mKS; + % Kernel in its grid space if flag == 1 - sections1 = [sections(2) sections(1) sections(3)]; - wgs84 = wgs84Ellipsoid("m"); - d = sqrt(X.^2 + Y.^2); - az = atan(X./Y)*360/2/pi; - az(isnan(az)) = 0; - [Y1,X1] = reckon(origin(1),origin(2),d,az,wgs84); + % The next figure checks the sensitivity of coda attenuation + % measurements. The code creates figures that show sections in the + % sensitivity kernels. The left panel shows the sensitivity kernel + % in the full space while the rigth panel shows the normalized + % kernel in the inversion grid. + + % reorder sections and compute geographic sampling + sections1 = [sections(2) sections(1) sections(3)]; + d = sqrt(X.^2 + Y.^2); + az = atan2d(X, Y); + az(isnan(az)) = 0; + wgs84 = wgs84Ellipsoid("m"); + [Y1,X1] = reckon(origin(1),origin(2),d,az,wgs84); - x1 = linspace(min(X1(:)),max(X1(:)),length(x))'; - y1 = linspace(min(Y1(:)),max(Y1(:)),length(y))'; - z1 = z; - [XEqS,YEqS,ZEqS] = meshgrid(x1,y1,z1); + % query grid in geographic coords and depth + x1 = linspace(min(X1(:)),max(X1(:)),length(x))'; + y1 = linspace(min(Y1(:)),max(Y1(:)),length(y))'; + z1 = z; + [XEqS,YEqS,ZEqS]= meshgrid(x1,y1,z1); - mEqSpace =... - griddata(X1,Y1,Z,log(mK),XEqS,YEqS,ZEqS); + % query grid in geographic coords and depth + mEqSpace = griddata(X1,Y1,Z,mK,XEqS,YEqS,ZEqS); - Xk1 = origin(2) + km2deg(Xk/1000); - Yk1 = origin(1) + km2deg(Yk/1000); + % kernel grid to geographic coords + Xk1 = origin(2) + km2deg(XkS/1000); + Yk1 = origin(1) + km2deg(YkS/1000); + + kernels = myfig(FName); subplot(1,2,1) - Murat_imageKernels(Xk1,Yk1,Zk,log(K),inferno,sections1) - SetFDefaults(); + Murat_imageKernels(Xk1,Yk1,ZkS/1000,KStart,inferno,sections1) subplot(1,2,2) Murat_imageKernels(XEqS,YEqS,ZEqS,mEqSpace,inferno,sections1) - + + Murat_saveFigures_2panels(kernels,pathFolder); end %pre-define 3D matrix in space -lx = length(x); -ly = length(y); -lz = length(z); -index = 0; -AQc_i = zeros(1,(length(modvQc(:,1)))); -for i=1:lx - for j=1:ly - for k=1:lz - index = index+1; - AQc_i(index) = mK(j,i,k); - end - end +AQc_i = reshape(permute(mK, [3 1 2]), [], 1); +posMask = AQc_i > 0; +if any(posMask) + minPos = min(AQc_i(posMask)); + AQc_i(AQc_i < 0)= minPos; end % Residual from cutting the grid (it is always < 1%). -AQc_i = AQc_i/sum(AQc_i); +AQc_i = AQc_i/sum(AQc_i); +end + +% --- Local helper to repair kernel when interp produced NaNs or zeros --- +function m = fixKernelLocal(m, K_grid, r_grid, xg, yg, zg, tiny) + % set NaNs and zeros to tiny + m(isnan(m)) = tiny; + m(m == 0) = tiny; + + % if still effectively empty (all tiny), place a single 1 at nearest node + if ~any(m > tiny) + modGrid = Murat_unfoldXYZ(xg, yg, zg); % N x 3 + % pick the original kernel's max location + [~, idxMax] = max(K_grid); + rmax = r_grid(idxMax, :); + dists = sum((modGrid(:,1:3) - rmax).^2, 2); + [~, imin] = min(dists); + tmp = zeros(size(modGrid,1),1); + tmp(imin) = 1; + [~,~,~,m] = Murat_fold(xg, yg, zg, tmp); + end end \ No newline at end of file diff --git a/bin/Murat_components.m b/bin/Murat_components.m index 99814a8..5b19216 100644 --- a/bin/Murat_components.m +++ b/bin/Murat_components.m @@ -23,200 +23,59 @@ % rapsp: energy ratio after averaging % rapspcn: coda to noise ratio after averaging -[dataL,lcf] = size(peakd1); -index = 0; +[dataL,lcf] = size(peakd1); +nGroups = dataL / comp; +idxOut = 0; + +% Preallocate outputs +peakd = nan(nGroups, lcf); +Qm = nan(nGroups, lcf); +RZZ = nan(nGroups, lcf); +rapsp = nan(nGroups, lcf); +rapspcn = nan(nGroups, lcf); for i = 1:comp:dataL - index = index+1; + idxOut = idxOut+1; + rows = i:(i+comp-1); - for j = 1:lcf - if comp == 2 - - noPD = compMissing(i,j,1); - noPD1 = compMissing(i+1,j,1); - noQc = compMissing(i,j,2); - noQc1 = compMissing(i+1,j,2); - noQ = compMissing(i,j,3); - noQ1 = compMissing(i+1,j,3); - - if noPD && noPD1 - peakd(index,j) = NaN; %#ok<*AGROW> - - elseif noPD && ~noPD1 - peakd(index,j) = peakd1(i+1,j); - - elseif ~noPD && noPD1 - peakd(index,j) = peakd1(i,j); - - else - peakd(index,j) =... - (peakd1(i,j) + peakd1(i+1,j))/2; - - end - - if noQc && noQc1 - Qm(index,j) = NaN; - RZZ(index,j) = NaN; - - elseif noQc && ~noQc1 - Qm(index,j) = Qm1(i+1,j); - RZZ(index,j) = RZZ1(i+1,j); - - elseif ~noQc && noQc1 - Qm(index,j) = Qm1(i,j); - RZZ(index,j) = RZZ1(i,j); - - else - Qm(index,j) =... - (Qm1(i,j) + Qm1(i+1,j))/2; - RZZ(index,j) =... - (RZZ1(i,j) + RZZ1(i+1,j))/2; - - end - - if noQ && noQ1 - rapsp(index,j) = NaN; - rapspcn(index,j) = NaN; - - elseif noQc && ~noQc1 - rapsp(index,j) = rapsp1(i+1,j); - rapspcn(index,j) = rapspcn1(i+1,j); - - elseif ~noQc && noQc1 - rapsp(index,j) = rapsp1(i,j); - rapspcn(index,j) = rapspcn1(i,j); - - else - rapsp(index,j) =... - (rapsp1(i,j) + rapsp1(i+1,j))/2; - rapspcn(index,j) =... - (rapspcn1(i,j) + rapspcn1(i+1,j))/2; - - end - - - elseif comp == 3 - - noPD = compMissing(i,j,1); - noPD1 = compMissing(i+1,j,1); - noPD2 = compMissing(i+2,j,1); - noQc = compMissing(i,j,2); - noQc1 = compMissing(i+1,j,2); - noQc2 = compMissing(i+2,j,2); - noQ = compMissing(i,j,3); - noQ1 = compMissing(i+1,j,3); - noQ2 = compMissing(i+2,j,3); - - if noPD && noPD1 && noPD2 - peakd(index,j) = NaN; - - elseif noPD && noPD1 && ~noPD2 - peakd(index,j) = peakd1(i+2,j); - - elseif noPD && ~noPD1 && noPD2 - peakd(index,j) = peakd1(i+1,j); - - elseif ~noPD && noPD1 && noPD2 - peakd(index,j) = peakd1(i,j); - - elseif ~noPD && ~noPD1 && noPD2 - peakd(index,j) =... - (peakd1(i,j) + peakd1(i+1,j))/2; - elseif ~noPD && noPD1 && ~noPD2 - peakd(index,j) =... - (peakd1(i,j) + peakd1(i+2,j))/2; - elseif noPD && ~noPD1 && ~noPD2 - peakd(index,j) =... - (peakd1(i+1,j) + peakd1(i+2,j))/2; - else - peakd(index,j) =... - (peakd1(i,j) + peakd1(i+1,j) + peakd1(i+2,j))/3; - - end - - if noQc && noQc1 && noQc2 - Qm(index,j) = NaN; - RZZ(index,j) = NaN; - - elseif noQc && noQc1 && ~noQc2 - Qm(index,j) = Qm1(i+2,j); - RZZ(index,j) = RZZ1(i+2,j); - - elseif noQc && ~noQc1 && noQc2 - Qm(index,j) = Qm1(i+1,j); - RZZ(index,j) = RZZ1(i+1,j); - - elseif ~noQc && noQc1 && noQc2 - Qm(index,j) = Qm1(i,j); - RZZ(index,j) = RZZ1(i,j); - - elseif ~noQc && ~noQc1 && noQc2 - Qm(index,j) =... - (Qm1(i,j) + Qm1(i+1,j))/2; - RZZ(index,j) =... - (RZZ1(i,j) + RZZ1(i+1,j))/2; - elseif ~noQc && noQc1 && ~noQc2 - Qm(index,j) =... - (Qm1(i,j) + Qm1(i+2,j))/2; - RZZ(index,j) =... - (RZZ1(i,j) + RZZ1(i+2,j))/2; - elseif noQc && ~noQc1 && ~noQc2 - Qm(index,j) =... - (Qm1(i+1,j) + Qm1(i+2,j))/2; - RZZ(index,j) =... - (RZZ1(i+1,j) + RZZ1(i+2,j))/2; - else - Qm(index,j) =... - (Qm1(i,j) + Qm1(i+1,j) + Qm1(i+2,j))/3; - RZZ(index,j) =... - (RZZ1(i,j) + RZZ1(i+1,j) + RZZ1(i+2,j))/3; - - end - - if noQ && noQ1 && noQ2 - rapsp(index,j) = NaN; - rapspcn(index,j) = NaN; - - elseif noQ && noQ1 && ~noQ2 - rapsp(index,j) = rapsp1(i+2,j); - rapspcn(index,j) = rapspcn1(i+2,j); - - elseif noQ && ~noQ1 && noQ2 - rapsp(index,j) = rapsp1(i+1,j); - RZZ(index,j) = rapspcn1(i+1,j); - - elseif ~noQ && noQ1 && noQ2 - rapsp(index,j) = rapsp1(i,j); - rapspcn(index,j) = rapspcn1(i,j); - - elseif ~noQ && ~noQ1 && noQ2 - rapsp(index,j) =... - (rapsp1(i,j) + rapsp1(i+1,j))/2; - rapspcn(index,j) =... - (rapspcn1(i,j) + rapspcn1(i+1,j))/2; - elseif ~noQ && noQ1 && ~noQ2 - rapsp(index,j) =... - (rapsp1(i,j) + rapsp1(i+2,j))/2; - rapspcn(index,j) =... - (rapspcn1(i,j) + rapspcn1(i+2,j))/2; - elseif noQ && ~noQ1 && ~noQ2 - rapsp(index,j) =... - (rapsp1(i+1,j) + rapsp1(i+2,j))/2; - rapspcn(index,j) =... - (rapspcn1(i+1,j) + rapspcn1(i+2,j))/2; - else - rapsp(index,j) =... - (rapsp1(i,j) + rapsp1(i+1,j) + rapsp1(i+2,j))/3; - rapspcn(index,j) =... - (rapspcn1(i,j) + rapspcn1(i+1,j) +... - rapspcn1(i+2,j))/3; - - end - - end - - end + % masks: valid (not missing) for each metric, size comp x lcf + validPD = ~squeeze(compMissing(rows,:,1)); % comp x lcf + validQc = ~squeeze(compMissing(rows,:,2)); + validRap = ~squeeze(compMissing(rows,:,3)); -end + % Extract data blocks comp x lcf + blockPD = peakd1(rows, :); + blockQm = Qm1(rows, :); + blockRZZ = RZZ1(rows, :); + blockRap = rapsp1(rows, :); + blockRapC = rapspcn1(rows, :); + + % For each column, average available values; if none available -> NaN + % peakd + sumPD = sum(blockPD .* validPD, 1); + nPD = sum(validPD, 1); + peakd(idxOut, :) = sumPD ./ nPD; + peakd(idxOut, nPD == 0) = NaN; + + % Qm and RZZ (independent averaging) + sumQ = sum(blockQm .* validQc, 1); + nQ = sum(validQc, 1); + Qm(idxOut, :) = sumQ ./ nQ; + Qm(idxOut, nQ == 0) = NaN; + + sumR = sum(blockRZZ .* validQc, 1); + RZZ(idxOut, :) = sumR ./ nQ; + RZZ(idxOut, nQ == 0)= NaN; + + % rapsp and rapspcn + sumRp = sum(blockRap .* validRap, 1); + nRp = sum(validRap, 1); + rapsp(idxOut, :) = sumRp ./ nRp; + rapsp(idxOut, nRp == 0) = NaN; + + sumRpc = sum(blockRapC .* validRap, 1); + rapspcn(idxOut, :) = sumRpc ./ nRp; + rapspcn(idxOut, nRp == 0) = NaN; + end diff --git a/bin/Murat_cornering.m b/bin/Murat_cornering.m index a39be9d..100b3f7 100644 --- a/bin/Murat_cornering.m +++ b/bin/Murat_cornering.m @@ -18,57 +18,57 @@ % flag: flags if ray outside of grid % Coordinates of the propagation grid are unwrapped -xGrid = gridD.x; -yGrid = gridD.y; -zGrid = gridD.z; +xGrid = gridD.x; +yGrid = gridD.y; +zGrid = gridD.z; % If point exceeds grid extrema -flag = 0; +flag = 0; % If statement to check if the point is outside the grid if zz < min(zGrid) - [~,ip] = min(abs(xx-xGrid)); - [~,jp] = min(abs(yy-yGrid)); - [~,kp] = min(zGrid); - flag = 1; + [~,ip] = min(abs(xx-xGrid)); + [~,jp] = min(abs(yy-yGrid)); + [~,kp] = min(zGrid); + flag = 1; return elseif zz > max(zGrid) - [~,ip] = min(abs(xx-xGrid)); - [~,jp] = min(abs(yy-yGrid)); - [~,kp] = max(zGrid); - flag = 1; + [~,ip] = min(abs(xx-xGrid)); + [~,jp] = min(abs(yy-yGrid)); + [~,kp] = max(zGrid); + flag = 1; return end if yy < min(yGrid) - [~,ip] = min(abs(xx-xGrid)); - [~,jp] = min(yGrid); - [~,kp] = min(abs(zz-zGrid)); - flag = 1; + [~,ip] = min(abs(xx-xGrid)); + [~,jp] = min(yGrid); + [~,kp] = min(abs(zz-zGrid)); + flag = 1; return elseif yy > max(yGrid) - [~,ip] = min(abs(xx-xGrid)); - [~,jp] = max(yGrid); - [~,kp] = min(abs(zz-zGrid)); - flag = 1; + [~,ip] = min(abs(xx-xGrid)); + [~,jp] = max(yGrid); + [~,kp] = min(abs(zz-zGrid)); + flag = 1; return end if xx < min(xGrid) - [~,ip] = min(xGrid); - [~,jp] = min(abs(yy-yGrid)); - [~,kp] = min(abs(zz-zGrid)); + [~,ip] = min(xGrid); + [~,jp] = min(abs(yy-yGrid)); + [~,kp] = min(abs(zz-zGrid)); flag=1; return elseif xx > max(xGrid) - [~,ip] = max(xGrid); - [~,jp] = min(abs(yy-yGrid)); - [~,kp] = min(abs(zz-zGrid)); - flag = 1; + [~,ip] = max(xGrid); + [~,jp] = min(abs(yy-yGrid)); + [~,kp] = min(abs(zz-zGrid)); + flag = 1; return end % Finds index - remember convention on shallowest SW point! -ip = find(xGridzz,1,'last'); +ip = find(xGridzz,1,'last'); end diff --git a/bin/Murat_data.m b/bin/Murat_data.m index 38ad67b..376917f 100644 --- a/bin/Murat_data.m +++ b/bin/Murat_data.m @@ -1,219 +1,251 @@ -function Murat = Murat_data(Murat) +function Murat = Murat_data(Murat) % MEASURES Qc, peak-delay and Q for each seismic trace located in a folder. -% This code is a collection of functions that do all the necessary. -% Inputs -listSac = Murat.input.listSac; -lengthData = length(listSac); - -compon = Murat.input.components; - -modv = Murat.input.modv; -lengthParameterModel = length(modv(:,1)); -gridStep = [Murat.input.gridStepX/2 ... - Murat.input.gridStepY/2 (modv(2,3) - modv(1,3))/2]; -modvQc = [modv(:,1) + gridStep(1)... +% Refactored input unpacking and preallocation +s = Murat.input; +FLabel = s.label; +workers = s.workers; %#ok + +% Basic lists and sizes +listSac = s.listSac; +nData = numel(listSac); +sacHeader = s.sacHeader; + +components = s.components; +sections = s.sections; +sections(3) = sections(3)/1000; +modv = s.modv; +nModelParams = size(modv(:,1),1); + +% Grid step and model grid coordinates +gridStepZ = (modv(2,3) - modv(1,3))/2; +gridStep = [s.gridStepX/2, s.gridStepY/2, gridStepZ]; +modvQc = [modv(:,1) + gridStep(1)... modv(:,2)+gridStep(2) modv(:,3)+gridStep(3)]; -gridD = Murat.input.gridPropagation; -pvel = Murat.input.pvel; - -cf = Murat.input.centralFrequency; -lcf = length(cf); - -workers = Murat.input.workers; -origin = Murat.input.origin; -originTime = Murat.input.originTime; -PTime = Murat.input.PTime; -STime = Murat.input.STime; -PorS = Murat.input.POrS; -tCm = Murat.input.startLapseTime; -vP = Murat.input.averageVelocityP; -vS = Murat.input.averageVelocityS; -maxtpde = Murat.input.maximumPeakDelay; -tWm = Murat.input.codaWindow; -sped = Murat.input.spectralDecay; -kT = Murat.input.kernelTreshold; -B0 = Murat.input.albedo; -Le1 = Murat.input.extinctionLength; -bodyWindow = Murat.input.bodyWindow; -startNoise = Murat.input.startNoise; -QcM = Murat.input.QcMeasurement; -lapseTimeMethod = Murat.input.lapseTimeMethod; -maxtravel = Murat.input.maxtravel; -mintravel = Murat.input.mintravel; - -% Set up variables to save -locationDeg = zeros(lengthData,6); -locationM = zeros(lengthData,6); -theoreticalTime = zeros(lengthData,1); -totalLengthRay = zeros(lengthData,1); -peakDelay = zeros(lengthData,lcf); -inverseQc = zeros(lengthData,lcf); -uncertaintyQc = zeros(lengthData,lcf); -energyRatioBodyCoda = zeros(lengthData,lcf); -energyRatioCodaNoise = zeros(lengthData,lcf); -raysPlot = zeros(100,5,lengthData); -tCoda = zeros(lengthData,lcf); - -inversionMatrixPeakDelay =... - zeros(lengthData,lengthParameterModel); -inversionMatrixQ =... - zeros(lengthData,lengthParameterModel); -inversionMatrixQc =... - zeros(lengthData,lengthParameterModel); -rayCrossing =... - zeros(lengthData,lengthParameterModel); -%========================================================================= +gridD = s.gridPropagation; +pvel = s.pvel; + +% Frequencies +cf = s.centralFrequency; +nCF = numel(cf); + +% Event/origin and timing +origin = s.origin; +originTime = s.originTime; +PTime = s.PTime; +STime = s.STime; +PorS = s.POrS; +startLapse = s.startLapseTime; + +% Velocities and travel times +vP = s.averageVelocityP; +vS = s.averageVelocityS; +maxtpde = s.maximumPeakDelay; +maxTravel = s.maxtravel; +minTravel = s.mintravel; +lapseTimeMethod = s.lapseTimeMethod; + +% Coda parameters +tCodaWindow = s.codaWindow; +kernelThresh = s.kernelTreshold; +B0 = s.albedo; +Le = s.iExtinctionLength; +bodyWindow = s.bodyWindow; +startNoise = s.startNoise; +QcMeasure = s.QcMeasurement; + + +% Preallocate outputs +locationDeg = zeros(nData,6); +locationM = zeros(nData,6); +theoreticalTime = zeros(nData,1); +totalLengthRay = zeros(nData,1); +peakDelay = nan(nData,nCF); +inverseQc = nan(nData,nCF); +uncertaintyQc = nan(nData,nCF); +energyRatioBCo = nan(nData,nCF); +energyRatioCNo = nan(nData,nCF); +raysPlot = zeros(100,5,nData); +tCoda = zeros(nData,nCF); + +inversionMatrixPD = zeros(nData,nModelParams); +inversionMatrixQ = zeros(nData,nModelParams); +inversionMatrixQc = zeros(nData,nModelParams,nCF); +rayCrossing = zeros(nData,nModelParams); + +storeFolderK = 'Kernels'; + % Count waveforms that must be eliminated because of peak-delay contraints % on peak delays and coda attenuation. -count_trash = 0; +countTrash = 0; +%========================================================================= +SAChdrList = cell(nData,1); +for k = 1:nData + fdl = sprintf('SAC_%g', k); + SAChdrList{k} = sacHeader.(fdl); +end -parfor (i = 1:lengthData,workers) +for i = 1:nData + % Progress every 100 traces if isequal(mod(i,100),0) - - disp(['Waveform number is ', num2str(i)]) + + fprintf('Waveform number is %d\n', i); end - listSac_i = listSac{i}; + listSac_i = listSac{i}; + SAChdr_i = SAChdrList{i}; + srate_i = 1/SAChdr_i.times.delta; - % Calculates envelopes - [tempis,sp_i,SAChdr_i,srate_i] = Murat_envelope(cf,listSac_i); + % Envelope and spectral preprocessing + [tempis,sp_i] = Murat_envelope(cf,listSac_i); - % Set earthquake and stations locations in degrees or meters - [locationDeg_i, locationM_i] =... - Murat_location(origin,SAChdr_i); - locationDeg(i,:) = locationDeg_i; + % Locations (deg and meters) + [locationDeg_i, locationM_i] = Murat_location(origin,SAChdr_i); + locationDeg(i,:) = locationDeg_i; - % Checks direct-wave picking on the trace and outputs it - [cursorPick_i, pktime_i, v_i] = Murat_picking(tempis,... + % Checks direct-wave picking + [cursorPick_i, pktime_i, v_i] = Murat_picking(tempis,... PTime,STime,PorS,vP,vS,srate_i,listSac_i,SAChdr_i); - % Conditions in case the zero time is missing in the header + % Missing origin-time corrections [theoreticalTime_i, originTime_i] =... Murat_originTime(pktime_i,originTime,v_i,locationM_i,SAChdr_i,i); - % Calculates the window where to search for peak delay - cursorPeakDelay_i =... + % Peak-delay search window and value + cursorPeakDelay_i =... Murat_peakDelayCheck(tempis,cursorPick_i,maxtpde,srate_i); - - % Calculates peak delay time - peakDelay_i =... + + peakDelay_i =... Murat_peakDelay(sp_i,cursorPick_i,srate_i,cursorPeakDelay_i); - % Calculates rays for the right component - calculateRays = recognizeComponents(i,compon); + % Decide if ray-dependent computations are required for this component + doRays = recognizeComponents(i, components); - if calculateRays - + if doRays % All the ray-dependent parameters - [Apd_i, AQ_i, totalLengthRay_i, raysPlot_i, rayCrossing_i]... - =... - Murat_rays(modv,gridD,pvel,locationM_i); - - inversionMatrixPeakDelay(i,:) = Apd_i; - inversionMatrixQ(i,:) = AQ_i; - totalLengthRay(i,1) = totalLengthRay_i; - raysPlot(:,:,i) = raysPlot_i; - rayCrossing(i,:) = rayCrossing_i; - end - - % Sets the lapse time - [tCoda_i, cursorCodaStart_i,... - cursorCodaEnd_i] =... - Murat_codaCheck(originTime_i,pktime_i,srate_i,tCm,tWm,tempis,... + [Apd_i, AQ_i, totalLengthRay_i, raysPlot_i, rayCrossing_i]=... + Murat_rays(modv,gridD,pvel,locationM_i); + inversionMatrixPD(i,:) = Apd_i; + inversionMatrixQ(i,:) = AQ_i; + totalLengthRay(i,1) = totalLengthRay_i; + raysPlot(:,:,i) = raysPlot_i; + rayCrossing(i,:) = rayCrossing_i; + end + % + % % Diffusion equation + % [storeD,residualD,outputSolverD,exitFlagSolverD] =... + % Murat_diffusion(cf,sp_i,startLapse,tCodaWindow,totalLengthRay_i); + + % Coda window, checks, and lapse time + [tCoda_i, cursorCodaStart_i,cursorCodaEnd_i] = Murat_codaCheck(... + originTime_i,pktime_i,srate_i,startLapse,tCodaWindow,tempis,... peakDelay_i,lapseTimeMethod); - if (cursorCodaEnd_i - cursorCodaStart_i) < (tWm*srate_i)-2 || ... - (pktime_i-originTime_i)>maxtravel || ... - (pktime_i-originTime_i) maxTravel || ... + (pktime_i-originTime_i) < minTravel + + locationM(i,:) = locationM_i; + theoreticalTime(i,1) = theoreticalTime_i; + tCoda(i,:) = tCoda_i; + countTrash = countTrash +1; continue end % Measures Qc and its uncertainty - [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,... - sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcM); + [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,... + sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasure); - % Decide if you calculate kernels - calculateKernels = recognizeComponents(i,compon); + % Decide if kernel/inversion matrix for Qc is needed + doKernels = doRays; - if calculateKernels + if doKernels + % Compute and plots kernels and coda matrix + TCodaStart = tCoda_i; + TCodaEnd = tCoda_i+tCodaWindow; + [KgridS, rgridS] =... + Murat_kernels(TCodaStart,locationM_i(1:3),locationM_i(4:6),... + modvQc,vS,kernelThresh,B0,Le,lapseTimeMethod); + [KgridE, rgridE] =... + Murat_kernels(TCodaEnd,locationM_i(1:3),locationM_i(4:6),... + modvQc,vS,kernelThresh,B0,Le,lapseTimeMethod); - % Calculates kernels - [K_grid, r_grid] =... - Murat_kernels(tCoda_i+tWm/2,locationM_i(1:3),... - locationM_i(4:6),modvQc,vS,kT,B0,Le1,lapseTimeMethod); + for j = 1:nCF + + cf_k = cf(j); + fcName = num2str(cf_k); + if find(fcName == '.') + fcName(fcName == '.') = '_'; + end + FName = ['Kernel_' fcName '_Hz']; + KS_i = KgridS(:,:,j); + KE_i = KgridE(:,:,j); + p = fullfile('./', FLabel, storeFolderK, FName); - % Calculates matrix - AQc_i =... - Murat_codaMatrix(modvQc,K_grid,r_grid,0,[],[]); - - inversionMatrixQc(i,:) = AQc_i; + AQc_i = Murat_codaMatrix(modvQc,KS_i,rgridS,KE_i,... + rgridE,i,origin,sections,FName,p); + inversionMatrixQc(i,:,j) = AQc_i; + + end end - % Measures Q + % Measure body/coda energy ratios [energyRatioBodyCoda_i,energyRatioCodaNoise_i]=... Murat_body(bodyWindow,startNoise,srate_i,sp_i,cursorPick_i,... cursorCodaStart_i,cursorCodaEnd_i); - % Saving - locationM(i,:) = locationM_i; - theoreticalTime(i,1) = theoreticalTime_i; - peakDelay(i,:) = peakDelay_i; - inverseQc(i,:) = inverseQc_i; - uncertaintyQc(i,:) = uncertaintyQc_i; - energyRatioBodyCoda(i,:) = energyRatioBodyCoda_i; - energyRatioCodaNoise(i,:) = energyRatioCodaNoise_i; - tCoda(i,:) = tCoda_i; + % Save results for this trace + locationM(i,:) = locationM_i; + theoreticalTime(i,1) = theoreticalTime_i; + peakDelay(i,:) = peakDelay_i; + inverseQc(i,:) = inverseQc_i; + uncertaintyQc(i,:) = uncertaintyQc_i; + energyRatioBCo(i,:) = energyRatioBodyCoda_i; + energyRatioCNo(i,:) = energyRatioCodaNoise_i; + tCoda(i,:) = tCoda_i; end -% Setting up the final data vectors and matrices with checks on values -Murat.rays.locationsDeg = locationDeg; -Murat.rays.locationsM = locationM; -Murat.rays.theoreticalTime = theoreticalTime; -Murat.PD.peakDelay = peakDelay; -Murat.PD.inversionMatrixPeakDelay = inversionMatrixPeakDelay; -Murat.Q.inversionMatrixQ = inversionMatrixQ; -Murat.rays.totalLengthRay = totalLengthRay; -Murat.rays.raysPlot = raysPlot; -Murat.rays.rayCrossing = sum(rayCrossing); -Murat.Qc.inverseQc = inverseQc; -Murat.Qc.uncertaintyQc = uncertaintyQc; -Murat.Qc.inversionMatrixQc = inversionMatrixQc; -Murat.Q.energyRatioBodyCoda = energyRatioBodyCoda; -Murat.Q.energyRatioCodaNoise = energyRatioCodaNoise; -Murat.Qc.tCoda = tCoda; - -Murat = Murat_selection(Murat); - -ratio = count_trash/lengthData*(100); -disp(['Ratio of removed recordings: ', num2str(ratio)]) - -if ~isempty(Murat.input.declustering) - Murat =... - Murat_declustering(Murat,Murat.input.declustering); +% Assign to Murat structure +Murat.rays.locationsDeg = locationDeg; +Murat.rays.locationsM = locationM; +Murat.rays.theoreticalTime = theoreticalTime; +Murat.PD.peakDelay = peakDelay; +Murat.PD.inversionMatrixPeakDelay = inversionMatrixPD; +Murat.Q.inversionMatrixQ = inversionMatrixQ; +Murat.rays.totalLengthRay = totalLengthRay; +Murat.rays.raysPlot = raysPlot; +Murat.rays.rayCrossing = sum(rayCrossing); +Murat.Qc.inverseQc = inverseQc; +Murat.Qc.uncertaintyQc = uncertaintyQc; +Murat.Qc.inversionMatrixQc = inversionMatrixQc; +Murat.Q.energyRatioBodyCoda = energyRatioBCo; +Murat.Q.energyRatioCodaNoise = energyRatioCNo; +Murat.Qc.tCoda = tCoda; + +Murat = Murat_selection(Murat); +ratio = countTrash/nData*(100); +fprintf('Ratio of recordings removed due to Qc or PD inputs: %.2f%%\n',... + ratio) + end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function calculateValue =... - recognizeComponents(index,components) +function doR = recognizeComponents(index,components) % LOGICAL to decide if forward model is necessary depending in waveform % number (index) and number of components. -calculateValue = isequal(components,1) ||... - (isequal(components,2) || isequal(components,3)) &&... - isequal(mod(index,components),1); +if components == 1 + doR = true; +elseif components == 2 || components == 3 + % Only process every components-th trace starting at 1 + doR = mod(index, components) == 1; +else + doR = false; +end +end diff --git a/bin/Murat_dataParallelized.m b/bin/Murat_dataParallelized.m index e1357d3..dc0b419 100644 --- a/bin/Murat_dataParallelized.m +++ b/bin/Murat_dataParallelized.m @@ -1,217 +1,251 @@ -function Murat = Murat_dataParallelized(Murat) +function Murat = Murat_dataParallelized(Murat) % MEASURES Qc, peak-delay and Q for each seismic trace located in a folder. -% This code is a collection of functions that do all the necessary. -% Inputs -listSac = Murat.input.listSac; -lengthData = length(listSac); - -compon = Murat.input.components; - -modv = Murat.input.modv; -lengthParameterModel = length(modv(:,1)); -gridStep = [Murat.input.gridStepX/2 ... - Murat.input.gridStepY/2 (modv(2,3) - modv(1,3))/2]; -modvQc = [modv(:,1) + gridStep(1)... +% Refactored input unpacking and preallocation +s = Murat.input; +FLabel = s.label; +workers = s.workers; + +% Basic lists and sizes +listSac = s.listSac; +nData = numel(listSac); +sacHeader = s.sacHeader; + +components = s.components; +sections = s.sections; +sections(3) = sections(3)/1000; +modv = s.modv; +nModelParams = size(modv(:,1),1); + +% Grid step and model grid coordinates +gridStepZ = (modv(2,3) - modv(1,3))/2; +gridStep = [s.gridStepX/2, s.gridStepY/2, gridStepZ]; +modvQc = [modv(:,1) + gridStep(1)... modv(:,2)+gridStep(2) modv(:,3)+gridStep(3)]; -gridD = Murat.input.gridPropagation; -pvel = Murat.input.pvel; - -cf = Murat.input.centralFrequency; -lcf = length(cf); - -workers = Murat.input.workers; -origin = Murat.input.origin; -originTime = Murat.input.originTime; -PTime = Murat.input.PTime; -STime = Murat.input.STime; -PorS = Murat.input.POrS; -tCm = Murat.input.startLapseTime; -vP = Murat.input.averageVelocityP; -vS = Murat.input.averageVelocityS; -maxtpde = Murat.input.maximumPeakDelay; -tWm = Murat.input.codaWindow; -sped = Murat.input.spectralDecay; -kT = Murat.input.kernelTreshold; -B0 = Murat.input.albedo; -Le1 = Murat.input.extinctionLength; -bodyWindow = Murat.input.bodyWindow; -startNoise = Murat.input.startNoise; -QcM = Murat.input.QcMeasurement; -lapseTimeMethod = Murat.input.lapseTimeMethod; -maxtravel = Murat.input.maxtravel; - -% Set up variables to save -locationDeg = zeros(lengthData,6); -locationM = zeros(lengthData,6); -theoreticalTime = zeros(lengthData,1); -totalLengthRay = zeros(lengthData,1); -peakDelay = zeros(lengthData,lcf); -inverseQc = zeros(lengthData,lcf); -uncertaintyQc = zeros(lengthData,lcf); -energyRatioBodyCoda = zeros(lengthData,lcf); -energyRatioCodaNoise = zeros(lengthData,lcf); -raysPlot = zeros(100,5,lengthData); -tCoda = zeros(lengthData,lcf); - -inversionMatrixPeakDelay =... - zeros(lengthData,lengthParameterModel); -inversionMatrixQ =... - zeros(lengthData,lengthParameterModel); -inversionMatrixQc =... - zeros(lengthData,lengthParameterModel); -rayCrossing =... - zeros(lengthData,lengthParameterModel); -%========================================================================= +gridD = s.gridPropagation; +pvel = s.pvel; + +% Frequencies +cf = s.centralFrequency; +nCF = numel(cf); + +% Event/origin and timing +origin = s.origin; +originTime = s.originTime; +PTime = s.PTime; +STime = s.STime; +PorS = s.POrS; +startLapse = s.startLapseTime; + +% Velocities and travel times +vP = s.averageVelocityP; +vS = s.averageVelocityS; +maxtpde = s.maximumPeakDelay; +maxTravel = s.maxtravel; +minTravel = s.mintravel; +lapseTimeMethod = s.lapseTimeMethod; + +% Coda parameters +tCodaWindow = s.codaWindow; +kernelThresh = s.kernelTreshold; +B0 = s.albedo; +Le = s.iExtinctionLength; +bodyWindow = s.bodyWindow; +startNoise = s.startNoise; +QcMeasure = s.QcMeasurement; + + +% Preallocate outputs +locationDeg = zeros(nData,6); +locationM = zeros(nData,6); +theoreticalTime = zeros(nData,1); +totalLengthRay = zeros(nData,1); +peakDelay = nan(nData,nCF); +inverseQc = nan(nData,nCF); +uncertaintyQc = nan(nData,nCF); +energyRatioBCo = nan(nData,nCF); +energyRatioCNo = nan(nData,nCF); +raysPlot = zeros(100,5,nData); +tCoda = zeros(nData,nCF); + +inversionMatrixPD = zeros(nData,nModelParams); +inversionMatrixQ = zeros(nData,nModelParams); +inversionMatrixQc = zeros(nData,nModelParams,nCF); +rayCrossing = zeros(nData,nModelParams); + +storeFolderK = 'Kernels'; + % Count waveforms that must be eliminated because of peak-delay contraints % on peak delays and coda attenuation. -count_trash = 0; +countTrash = 0; +%========================================================================= +SAChdrList = cell(nData,1); +for k = 1:nData + fdl = sprintf('SAC_%g', k); + SAChdrList{k} = sacHeader.(fdl); +end -parfor (i = 1:lengthData,workers) +parfor (i = 1:nData,workers) + % Progress every 100 traces if isequal(mod(i,100),0) - - disp(['Waveform number is ', num2str(i)]) + + fprintf('Waveform number is %d\n', i); end - listSac_i = listSac{i}; + listSac_i = listSac{i}; + SAChdr_i = SAChdrList{i}; + srate_i = 1/SAChdr_i.times.delta; - % Calculates envelopes - [tempis,sp_i,SAChdr_i,srate_i] = Murat_envelope(cf,listSac_i); + % Envelope and spectral preprocessing + [tempis,sp_i] = Murat_envelope(cf,listSac_i); - % Set earthquake and stations locations in degrees or meters - [locationDeg_i, locationM_i] =... - Murat_location(origin,SAChdr_i); - locationDeg(i,:) = locationDeg_i; + % Locations (deg and meters) + [locationDeg_i, locationM_i] = Murat_location(origin,SAChdr_i); + locationDeg(i,:) = locationDeg_i; - % Checks direct-wave picking on the trace and outputs it - [cursorPick_i, pktime_i, v_i] = Murat_picking(tempis,... + % Checks direct-wave picking + [cursorPick_i, pktime_i, v_i] = Murat_picking(tempis,... PTime,STime,PorS,vP,vS,srate_i,listSac_i,SAChdr_i); - % Conditions in case the zero time is missing in the header + % Missing origin-time corrections [theoreticalTime_i, originTime_i] =... Murat_originTime(pktime_i,originTime,v_i,locationM_i,SAChdr_i,i); - % Calculates the window where to search for peak delay - cursorPeakDelay_i =... + % Peak-delay search window and value + cursorPeakDelay_i =... Murat_peakDelayCheck(tempis,cursorPick_i,maxtpde,srate_i); - - % Calculates peak delay time - peakDelay_i =... + + peakDelay_i =... Murat_peakDelay(sp_i,cursorPick_i,srate_i,cursorPeakDelay_i); - % Calculates rays for the right component - calculateRays = recognizeComponents(i,compon); + % Decide if ray-dependent computations are required for this component + doRays = recognizeComponents(i, components); - if calculateRays - + if doRays % All the ray-dependent parameters - [Apd_i, AQ_i, totalLengthRay_i, raysPlot_i, rayCrossing_i]... - =... - Murat_rays(modv,gridD,pvel,locationM_i); - - inversionMatrixPeakDelay(i,:) = Apd_i; - inversionMatrixQ(i,:) = AQ_i; - totalLengthRay(i,1) = totalLengthRay_i; - raysPlot(:,:,i) = raysPlot_i; - rayCrossing(i,:) = rayCrossing_i; - end - - % Sets the lapse time - [tCoda_i, cursorCodaStart_i,... - cursorCodaEnd_i] =... - Murat_codaCheck(originTime_i,pktime_i,srate_i,tCm,tWm,tempis,... + [Apd_i, AQ_i, totalLengthRay_i, raysPlot_i, rayCrossing_i]=... + Murat_rays(modv,gridD,pvel,locationM_i); + inversionMatrixPD(i,:) = Apd_i; + inversionMatrixQ(i,:) = AQ_i; + totalLengthRay(i,1) = totalLengthRay_i; + raysPlot(:,:,i) = raysPlot_i; + rayCrossing(i,:) = rayCrossing_i; + end + % + % % Diffusion equation + % [storeD,residualD,outputSolverD,exitFlagSolverD] =... + % Murat_diffusion(cf,sp_i,startLapse,tCodaWindow,totalLengthRay_i); + + % Coda window, checks, and lapse time + [tCoda_i, cursorCodaStart_i,cursorCodaEnd_i] = Murat_codaCheck(... + originTime_i,pktime_i,srate_i,startLapse,tCodaWindow,tempis,... peakDelay_i,lapseTimeMethod); - if (cursorCodaEnd_i - cursorCodaStart_i) < (tWm*srate_i)-2 || ... - (pktime_i-originTime_i)>maxtravel - - locationM(i,:) = locationM_i; - theoreticalTime(i,1) = theoreticalTime_i; - peakDelay(i,:) = NaN; - inverseQc(i,:) = NaN; - uncertaintyQc(i,:) = NaN; - energyRatioBodyCoda(i,:) = NaN; - energyRatioCodaNoise(i,:) = NaN; - tCoda(i,:) = tCoda_i; - - count_trash = count_trash +1; + % Reject traces that don't meet coda/travel requirements + codaSamples = cursorCodaEnd_i - cursorCodaStart_i; + if mean(codaSamples) < (tCodaWindow*srate_i) - 2 || ... + (pktime_i-originTime_i) > maxTravel || ... + (pktime_i-originTime_i) < minTravel + + locationM(i,:) = locationM_i; + theoreticalTime(i,1) = theoreticalTime_i; + tCoda(i,:) = tCoda_i; + countTrash = countTrash +1; continue end % Measures Qc and its uncertainty - [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,sped,... - sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcM); + [inverseQc_i, uncertaintyQc_i] = Murat_Qc(cf,... + sp_i,cursorCodaStart_i,cursorCodaEnd_i,tCoda_i,srate_i,QcMeasure); - % Decide if you calculate kernels - calculateKernels = recognizeComponents(i,compon); + % Decide if kernel/inversion matrix for Qc is needed + doKernels = doRays; - if calculateKernels + if doKernels + % Compute and plots kernels and coda matrix + TCodaStart = tCoda_i; + TCodaEnd = tCoda_i+tCodaWindow; + [KgridS, rgridS] =... + Murat_kernels(TCodaStart,locationM_i(1:3),locationM_i(4:6),... + modvQc,vS,kernelThresh,B0,Le,lapseTimeMethod); + [KgridE, rgridE] =... + Murat_kernels(TCodaEnd,locationM_i(1:3),locationM_i(4:6),... + modvQc,vS,kernelThresh,B0,Le,lapseTimeMethod); - % Calculates kernels - [K_grid, r_grid] =... - Murat_kernels(tCoda_i+tWm/2,locationM_i(1:3),... - locationM_i(4:6),modvQc,vS,kT,B0,Le1,lapseTimeMethod); + for j = 1:nCF + + cf_k = cf(j); + fcName = num2str(cf_k); + if find(fcName == '.') + fcName(fcName == '.') = '_'; + end + FName = ['Kernel_' fcName '_Hz']; + KS_i = KgridS(:,:,j); + KE_i = KgridE(:,:,j); + p = fullfile('./', FLabel, storeFolderK, FName); - % Calculates matrix - AQc_i =... - Murat_codaMatrix(modvQc,K_grid,r_grid,0,[],[]); - - inversionMatrixQc(i,:) = AQc_i; + AQc_i = Murat_codaMatrix(modvQc,KS_i,rgridS,KE_i,... + rgridE,i,origin,sections,FName,p); + inversionMatrixQc(i,:,j) = AQc_i; + + end end - % Measures Q + % Measure body/coda energy ratios [energyRatioBodyCoda_i,energyRatioCodaNoise_i]=... Murat_body(bodyWindow,startNoise,srate_i,sp_i,cursorPick_i,... cursorCodaStart_i,cursorCodaEnd_i); - % Saving - locationM(i,:) = locationM_i; - theoreticalTime(i,1) = theoreticalTime_i; - peakDelay(i,:) = peakDelay_i; - inverseQc(i,:) = inverseQc_i; - uncertaintyQc(i,:) = uncertaintyQc_i; - energyRatioBodyCoda(i,:) = energyRatioBodyCoda_i; - energyRatioCodaNoise(i,:) = energyRatioCodaNoise_i; - tCoda(i,:) = tCoda_i; + % Save results for this trace + locationM(i,:) = locationM_i; + theoreticalTime(i,1) = theoreticalTime_i; + peakDelay(i,:) = peakDelay_i; + inverseQc(i,:) = inverseQc_i; + uncertaintyQc(i,:) = uncertaintyQc_i; + energyRatioBCo(i,:) = energyRatioBodyCoda_i; + energyRatioCNo(i,:) = energyRatioCodaNoise_i; + tCoda(i,:) = tCoda_i; end -% Setting up the final data vectors and matrices with checks on values -Murat.data.locationsDeg = locationDeg; -Murat.data.locationsM = locationM; -Murat.data.theoreticalTime = theoreticalTime; -Murat.data.peakDelay = peakDelay; -Murat.data.inversionMatrixPeakDelay = inversionMatrixPeakDelay; -Murat.data.inversionMatrixQ = inversionMatrixQ; -Murat.data.totalLengthRay = totalLengthRay; -Murat.data.raysPlot = raysPlot; -Murat.data.rayCrossing = sum(rayCrossing); -Murat.data.inverseQc = inverseQc; -Murat.data.uncertaintyQc = uncertaintyQc; -Murat.data.inversionMatrixQc = inversionMatrixQc; -Murat.data.energyRatioBodyCoda = energyRatioBodyCoda; -Murat.data.energyRatioCodaNoise = energyRatioCodaNoise; -Murat.data.tCoda = tCoda; - -Murat = Murat_selection(Murat); - -ratio = count_trash/lengthData*(100); -disp(['Ratio of removed recordings: ', num2str(ratio)]) - -if ~isempty(Murat.input.declustering) - Murat =... - Murat_declustering(Murat,Murat.input.declustering); +% Assign to Murat structure +Murat.rays.locationsDeg = locationDeg; +Murat.rays.locationsM = locationM; +Murat.rays.theoreticalTime = theoreticalTime; +Murat.PD.peakDelay = peakDelay; +Murat.PD.inversionMatrixPeakDelay = inversionMatrixPD; +Murat.Q.inversionMatrixQ = inversionMatrixQ; +Murat.rays.totalLengthRay = totalLengthRay; +Murat.rays.raysPlot = raysPlot; +Murat.rays.rayCrossing = sum(rayCrossing); +Murat.Qc.inverseQc = inverseQc; +Murat.Qc.uncertaintyQc = uncertaintyQc; +Murat.Qc.inversionMatrixQc = inversionMatrixQc; +Murat.Q.energyRatioBodyCoda = energyRatioBCo; +Murat.Q.energyRatioCodaNoise = energyRatioCNo; +Murat.Qc.tCoda = tCoda; + +Murat = Murat_selection(Murat); +ratio = countTrash/nData*(100); +fprintf('Ratio of recordings removed due to Qc or PD inputs: %.2f%%\n',... + ratio) + end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function calculateValue =... - recognizeComponents(index,components) +function doR = recognizeComponents(index,components) % LOGICAL to decide if forward model is necessary depending in waveform % number (index) and number of components. -calculateValue = isequal(components,1) ||... - (isequal(components,2) || isequal(components,3)) &&... - isequal(mod(index,components),1); +if components == 1 + doR = true; +elseif components == 2 || components == 3 + % Only process every components-th trace starting at 1 + doR = mod(index, components) == 1; +else + doR = false; +end +end \ No newline at end of file diff --git a/bin/Murat_dataWarning.m b/bin/Murat_dataWarning.m index 43cdc89..069a2e2 100644 --- a/bin/Murat_dataWarning.m +++ b/bin/Murat_dataWarning.m @@ -1,9 +1,9 @@ function [problempd,problemQc,problemRZZ,problemQ,yes_pd,compMissing,... flag] = Murat_dataWarning(listaSac,tresholdnoise,... - maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,comp,flag,QcM) + maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,rapsp,comp,flag,QcM) % function [problempd,problemQc,problemRZZ,problemQ,yes_pd,compMissing,... % flag] = Murat_dataWarning(listaSac,tresholdnoise,... -% maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,comp,flag,QcM) +% maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,rapsp,comp,flag,QcM) % % WARNS about problems with the data and locates indices where this happens % @@ -17,6 +17,7 @@ % Qm: coda attenuation values % RZZ: uncertainty on Qc % rapspcn: coda to noise ratio +% rapsp: P/S to coda ratio % comp: components % flag: flag to change between one or more components % QcM: chosen method to measure Qc between Lin e NonLin @@ -30,98 +31,91 @@ % compMissing: missing components after processing % flag: flag to discriminate one-component from three -lcf = size(Qm); -problempd = cell(1,lcf(2)); -problemQc = cell(1,lcf(2)); -problemRZZ = cell(1,lcf(2)); -problemQ = cell(1,lcf(2)); +% sizes +[nRows, nCols] = size(Qm); -yes_pd = peakd > miPD & peakd < maPD; -no_pd = peakd > maPD | peakd < miPD; -no_Qc = Qm == 0; -no_Q = rapspcn < tresholdnoise; - -compMissing(:,:,1) = no_pd; -compMissing(:,:,2) = no_Qc; -compMissing(:,:,3) = no_Q; +% logical masks +yes_pd = (peakd > miPD) & (peakd < maPD); +no_pd = ~yes_pd; +no_Qc = (Qm == 0); if isequal(QcM,'Linearized') - no_RZZ = RZZ <= fT; + no_RZZ = (RZZ <= fT); elseif isequal(QcM,'NonLinear') - no_RZZ = no_Qc; - + no_RZZ = (RZZ >= fT); + end -for i = 1:lcf(2) - - problempd{i} = listaSac(no_pd(:,i)); - problemQc{i} = listaSac(no_Qc(:,i)); - problemRZZ{i} = listaSac(no_RZZ(:,i)); - problemQ{i} = listaSac(no_Q(:,i)); + + +no_Q = (rapspcn < tresholdnoise)|(rapsp < tresholdnoise); + +compMissing = false(nRows, nCols, 3); +compMissing(:,:,1) = no_pd; +compMissing(:,:,2) = no_Qc; +compMissing(:,:,3) = no_Q; + +problempd = cell(1,nCols); +problemQc = cell(1,nCols); +problemRZZ = cell(1,nCols); +problemQ = cell(1,nCols); + +for c = 1:nCols + idx_pd = no_pd(:,c); + idx_Qc = no_Qc(:,c); + idx_RZZ = no_RZZ(:,c); + idx_Q = no_Q(:,c); + + problempd{c} = listaSac(idx_pd); + problemQc{c} = listaSac(idx_Qc); + problemRZZ{c} = listaSac(idx_RZZ); + problemQ{c} = listaSac(idx_Q); end +% Percentages (per frequency / column) +pctNoQc = sum(no_Qc) / nRows * 100; +pctNoRZZ = sum(no_RZZ) / nRows * 100; +pctNoPD = sum(no_pd) / nRows * 100; +pctNoQ = sum(no_Q) / nRows * 100; + + % Displays different messages in case of more than 1 component -if comp == 1 && ~isequal(flag,2) - displayNoQc = sum(no_Qc)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQc),... - '] % of your Qc are = 0']); - - displayNoRZZ = sum(no_RZZ)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoRZZ),... - ']% of your correlation coefficients are below accuracy treshold']); - - displayNoPD = sum(no_pd)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoPD),... - '] % of your peak delays are outside peak delay limits']); - - displayNoQ = sum(no_Q)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQ),... - ']% of your coda-to-noise ratios are below noise treshold']); - - flag = 2; - +if comp == 1 && flag ~= 2 + % When single component processing and flag not equal 2, show overall percentages + fprintf('[%s] %% of Qc are below threshold\n', num2str(pctNoQc)); + fprintf('[%s] %% of Qc uncertainties are below threshold\n', num2str(pctNoRZZ)); + fprintf('[%s] %% of peak delays are outside limits\n', num2str(pctNoPD)); + fprintf('[%s] %% of ratios are below threshold\n', num2str(pctNoQ)); + flag = 2; + else + % compute means once + meanQc = mean(pctNoQc); + meanRZZ = mean(pctNoRZZ); + meanPD = mean(pctNoPD); + meanQ = mean(pctNoQ); + switch flag case 0 - - displayNoQc = sum(no_Qc)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQc),... - '] % of your original Qc are = 0']); - - displayNoRZZ = sum(no_RZZ)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoRZZ),... - ']% of your original correlation coefficients are below treshold']); - - displayNoPD = sum(no_pd)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoPD),... - '] % of your original peak delays are outside peak delay limits']); - - displayNoQ = sum(no_Q)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQ),... - ']% of your original coda-to-noise ratios are below treshold']); - - disp(['Processing to see how many data you have when considering '... - str2double(comp) ' components']) - - flag = 1; + fprintf('[%g] %% of Qc are below threshold\n', meanQc); + fprintf('[%g] %% of Qc uncertainties are below threshold\n', meanRZZ); + fprintf('[%g] %% of peak delays are outside limits\n', meanPD); + fprintf('[%g] %% of ratios are below threshold\n', meanQ); + + if isnumeric(comp) + ncompStr = num2str(comp); + else + ncompStr = comp; + end + fprintf('Processing to see how many data you have when considering %s components\n', ncompStr); + flag = 1; + case 1 - - displayNoQc = sum(no_Qc)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQc),... - '] % of your Qc are = 0']); - - displayNoRZZ = sum(no_RZZ)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoRZZ),... - ']% of your correlation coefficients are below accuracy treshold']); - - displayNoPD = sum(no_pd)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoPD),... - '] % of your peak delays are outside peak delay limits']); - - displayNoQ = sum(no_Q)/lcf(1)*100; - disp(['In your frequency range, [',num2str(displayNoQ),... - ']% of your coda-to-noise ratios are below noise treshold']); + fprintf('[%g] %% of Qc are below threshold\n', meanQc); + fprintf('[%g] %% of Qc uncertainties are below threshold\n', meanRZZ); + fprintf('[%g] %% of peak delays are outside limits\n', meanPD); + fprintf('[%g] %% of ratios are below threshold\n', meanQ); end end \ No newline at end of file diff --git a/bin/Murat_declustering.m b/bin/Murat_declustering.m index 05eb771..ef65437 100644 --- a/bin/Murat_declustering.m +++ b/bin/Murat_declustering.m @@ -1,41 +1,28 @@ -function Murat = Murat_declustering(Murat,factor) -% function Murat = Murat_declustering(Murat,factor) +function indKeep = Murat_declustering(origin,ending,x,y,z,QcM,uncertainty,locationsDeg,factor) +% indKeep = Murat_declustering(origin,ending,x,y,z,QcM,uncertainty,locationsDeg,factor) % -% SAVES declustered variables and PLOTS rays before and after declustering. +% SAVES declustered variables efore and after declustering. % The declustering is based on dividing the original grid into finer node -% spacing and selecting those with the best coda decay. +% spacing and selecting those with the least coda uncertainty. % % Input parameters: % Murat: Murat structure variable % factor: input factor used to divide the original grid -components = Murat.input.components; -origin = Murat.input.origin; -ending = Murat.input.end; -x = Murat.input.x; -y = Murat.input.y; -z = Murat.input.z; - -locationsDeg = Murat.rays.locationsDeg; -uncertaintyQc = Murat.Qc.uncertaintyQc; -Murat.rays.locDegOriginal = locationsDeg; - -disp(['used waveforms before declustering: ',... - num2str(length(locationsDeg)*components)]) - % create smaller grid to cluster events -lat_step = length(y)*factor; -lon_step = length(x)*factor; -z_step = length(z)*factor; -lat_grid = linspace(origin(2),ending(2),lat_step); -lon_grid = linspace(origin(1),ending(1),lon_step); -z_grid = -linspace(origin(3),ending(3),z_step); +lat_step = length(y)*factor; +lon_step = length(x)*factor; +z_step = length(z)*factor; -% decimate evestaz to one entry per event - station pair -evestaz = unique(locationsDeg,'rows','stable'); +lat_grid = linspace(origin(2),ending(2),lat_step); +lon_grid = linspace(origin(1),ending(1),lon_step); +z_grid = -linspace(origin(3),ending(3),z_step); -%% loop around new grid -new_evestaz = []; +% decimate locationsDeg to one entry per event - station pair +locationsDeg = unique(locationsDeg,'rows','stable'); + +indKeep = false(length(uncertainty),1); +new_locationsDeg= []; for i = 1:lon_step-1 @@ -43,54 +30,38 @@ for k = 1:z_step-1 % find all events within one grid cell - find_evs = find(evestaz(:,1)>lon_grid(i) & ... - evestaz(:,1)lat_grid(j) & evestaz(:,2)z_grid(k) & evestaz(:,3)lon_grid(i) & ... + locationsDeg(:,1)lat_grid(j) &... + locationsDeg(:,2)z_grid(k) &... + locationsDeg(:,3) + % event/station pair with lowest uncertainty on Qc + d = sortrows(events, [4 7]); + [~, ia, ~]= unique(d(:,4),'rows','last'); + to_keep = d(ia,:); + new_locationsDeg= [new_locationsDeg;to_keep]; %#ok end end end end -new_evestaz = sortrows(new_evestaz,8); +new_locationsDeg = sortrows(new_locationsDeg,8); -%% remove deleted data from Murat structure variable -ind_to_keep = new_evestaz(:,8); -Murat.Q.energyRatioBodyCoda =... - Murat.Q.energyRatioBodyCoda(ind_to_keep,:); -Murat.Q.energyRatioCodaNoise=... - Murat.Q.energyRatioCodaNoise(ind_to_keep,:); -Murat.Qc.inverseQc = Murat.Qc.inverseQc(ind_to_keep,:); -Murat.PD.inversionMatrixPeakDelay... - =... - Murat.PD.inversionMatrixPeakDelay(ind_to_keep,:); -Murat.Q.inversionMatrixQ = Murat.Q.inversionMatrixQ(ind_to_keep,:); -Murat.Qc.inversionMatrixQc = Murat.Qc.inversionMatrixQc(ind_to_keep,:); -Murat.rays.locationsDeg = Murat.rays.locationsDeg(ind_to_keep,:); -Murat.PD.peakDelay = Murat.PD.peakDelay(ind_to_keep,:); -Murat.PD.retainPeakDelay = Murat.PD.retainPeakDelay(ind_to_keep,:); -Murat.Q.retainQ = Murat.Q.retainQ(ind_to_keep,:); -Murat.Qc.retainQc = Murat.Qc.retainQc(ind_to_keep,:); -Murat.Qc.tCoda = Murat.Qc.tCoda(ind_to_keep,:); -Murat.rays.totalLengthRay = Murat.rays.totalLengthRay(ind_to_keep,:); -Murat.rays.travelTime = Murat.rays.travelTime(ind_to_keep,:); -Murat.Qc.uncertaintyQc = Murat.Qc.uncertaintyQc(ind_to_keep,:); -Murat.PD.variationPeakDelay = Murat.PD.variationPeakDelay(ind_to_keep,:); +indKeep(new_locationsDeg(:,8)) = true; -disp(['used waveforms after declustering: ',... - num2str(length(ind_to_keep)*components)]) end \ No newline at end of file diff --git a/bin/Murat_diffusion.m b/bin/Murat_diffusion.m new file mode 100644 index 0000000..f43665e --- /dev/null +++ b/bin/Murat_diffusion.m @@ -0,0 +1,53 @@ +function [storeD,residualD,outputSolverD,exitFlagSolverD] =... + Murat_diffusion(cf,sp_i,t,tW,r) +% function [storeD,residualD,outputSolverD,exitFlagSolverD] =... +% Murat_diffusion(cf,sp_i,t,tW,r) +% +% CALCULATES the diffusion constant +% +% Input parameters: +% cf: central frequency +% sp_i: envelopes at different frequencies +% t: lapse time +% tW: coda window +% r: ray length +% +% Outputs: +% storeD: best-fit D per frequency +% residualD: objective value (squared error) per frequency +% outputSolverD: struct with solver info per frequency +% exitFlagSolverD:struct with exit flag (0/1 info) per frequency + + +% E(x,t1) = W./(4*pi*D*t1)^1.5*exp(-r^2/D/t1) +% E(x,t2) = W./(4*pi*D*t2)^1.5*exp(-r^2/D/t2) +% E(x,t1)/E(x,t2) = (4*pi*D*t2)^1.5/(4*pi*D*t1)^1.5*exp(-r^2/D*[1/t1-1/t2]) + +nF = numel(cf); +storeD = zeros(1,nF); +residualD = zeros(1,nF); +outputSolverD = struct(); +exitFlagSolverD = struct(); + +ratioE = sp_i(1,:)./sp_i(end,:); +tcW = t + tW; +r = r/1000; +for k = 1:nF + ratioE_k = ratioE(k); + D = optimvar('D',1,LowerBound=10^-7,UpperBound=10^3); + obj = norm(ratioE_k-(4*pi*D*tcW)^1.5/(4*pi*D*t)^1.5*... + exp(-r^2/D*(1/t-1/tcW))); + prob = optimproblem('Objective',obj); + x0.D = 10^-2; + fld = sprintf('Hz%g', cf(k)); + fld = strrep(fld, '.', '_'); + + options = optimoptions('fmincon','Algorithm','sqp'); + [sol,fval,exitflag,output] = solve(prob,x0, 'Options',options); + + storeD(k) = sol.D; + residualD(k) = fval; + outputSolverD.(fld) = output; + exitFlagSolverD.(fld) = exitflag; + +end \ No newline at end of file diff --git a/bin/Murat_envelope.m b/bin/Murat_envelope.m index 2922c9f..b34c41a 100644 --- a/bin/Murat_envelope.m +++ b/bin/Murat_envelope.m @@ -1,45 +1,81 @@ -function [tempis, sp_i,SAChdr_i,srate_i] = Murat_envelope(cf,listSac_i) -% function [tempis, sp_i,SAChdr_i,srate_i] = Murat_envelope(cf,listSac_i) -% -% ENVELOPE calculation for all frequencies, also outputs properties of seismograms +function [tempis, sp_i] = Murat_envelope(cf, listSac_i, envelopeSmoothTime) +% MURAT_ENVELOPE +% Compute the signal envelope for all frequency bands. % % Input parameters: -% cf: central frequency -% listSac_i: list of SAC files -% +% cf: central frequencies +% listSac_i: SAC file name +% envelopeSmoothTime: envelope smoothing window in seconds +% % Output parameters: -% tempis: time vector of seismogram -% sp_i: envelopes at different frequencies -% SAChdr_i: SAC header -% srate_i: sampling rate - - -lcf = length(cf); -[tempis,sisma,SAChdr_i] = fget_sac(listSac_i); -srate_i = 1/SAChdr_i.times.delta; -sisma = detrend(sisma,1); -lsis = length(sisma); -tu = tukeywin(lsis,0.05); -tsisma = tu.*sisma; -sp_i = zeros(lsis,lcf); - -%% Calculations - -if isequal(srate_i,-12345) - error(['Waveform ' listaSac_i 'has no sampling rate!']) +% tempis: seismogram time vector +% sp_i: squared envelopes for the different frequencies + +%%% MODIFICATION: +%%% Added envelopeSmoothTime as a third input argument. +%%% If not provided, a default value of 1.0 s is used +%%% to preserve the original behavior. + +if nargin < 3 || isempty(envelopeSmoothTime) + envelopeSmoothTime = 1.0; end -for i = 1:lcf - % Filter creation - in loop for different frequencies - Wn =... - ([cf(i)-cf(i)/3 cf(i)+cf(i)/3]/srate_i*2); - [z,p,k] = butter(4,Wn,'bandpass'); - [sos,g] = zp2sos(z,p,k); - - fsisma = filtfilt(sos,g,tsisma); - - [sp,~] =... - envelope(fsisma,round(srate_i),'rms'); - sp_i(:,i) = sp.^2; +lcf = length(cf); + +[tempis, sisma, SAChdr_i] = fget_sac(listSac_i); +srate_i = 1 / SAChdr_i.times.delta; + +sisma = detrend(sisma,1); +lsis = length(sisma); + +tu = tukeywin(lsis, 0.05); +tsisma = tu .* sisma; + +sp_i = zeros(lsis, lcf); + +%%% MODIFICATION: +%%% Improved validation of the sampling rate. +%%% Previously, only the value -12345 was checked. +if isequal(srate_i, -12345) || ~isfinite(srate_i) || srate_i <= 0 + error(['Waveform ' listSac_i ' has no valid sampling rate!']) end + +%%% MODIFICATION: +%%% Convert the smoothing time (in seconds) to a number of samples. +%%% Example: 0.25 s * 100 Hz = 25 samples. +winSamples = max(1, round(envelopeSmoothTime * srate_i)); + +for i = 1:lcf + + % Create the bandpass filter for the current frequency + Wn = [cf(i)-cf(i)/3, cf(i)+cf(i)/3] / srate_i * 2; + + %%% MODIFICATION: + %%% Ensure a valid frequency band to avoid butter filter errors. + if Wn(1) <= 0 + Wn(1) = 0.001; + end + if Wn(2) >= 1 + Wn(2) = 0.999; + end + if Wn(2) <= Wn(1) + warning('Skipping frequency %.2f Hz for %s: invalid bandpass.', cf(i), listSac_i); + continue + end + + [z,p,k] = butter(4, Wn, 'bandpass'); + [sos,g] = zp2sos(z,p,k); + + fsisma = filtfilt(sos, g, tsisma); + + %%% MODIFICATION: + %%% Previously, MuRAT used round(srate_i), corresponding + %%% to an approximately 1-second smoothing window. + %%% The window length is now controlled by the input parameter. + [sp, ~] = envelope(fsisma, winSamples, 'rms'); + + % Compute energy as squared envelope + sp_i(:,i) = sp.^2; end + +end \ No newline at end of file diff --git a/bin/Murat_fold.m b/bin/Murat_fold.m index 6af2d9b..69ce1b9 100644 --- a/bin/Murat_fold.m +++ b/bin/Murat_fold.m @@ -15,25 +15,22 @@ % Z: 3D z matrix in meshgrid format % VMesh: 3D field matrix in meshgrid format -lx = length(x); -ly = length(y); -lz = length(z); -VMesh = zeros(ly,lx,lz); +x = x(:).'; +y = y(:).'; +z = z(:).'; + +lx = numel(x); +ly = numel(y); +lz = numel(z); [X,Y,Z] = meshgrid(x,y,z); - -if (nargin==4) - index = 0; - for i=1:lx - for j=1:ly - for k=1:lz - index = index+1; - VMesh(j,i,k)= v(index); - end - end - end -end +% Ensure the field vector v is provided; if not, initialize VMesh to empty +if nargin < 4 + VMesh = []; +else + VMesh = permute(reshape(v, [lz, ly, lx]), [2,3,1]); +end end \ No newline at end of file diff --git a/bin/Murat_hitmap.m b/bin/Murat_hitmap.m new file mode 100644 index 0000000..0416334 --- /dev/null +++ b/bin/Murat_hitmap.m @@ -0,0 +1,31 @@ +function Murat_hitmap(CoordGrid,rayCrossing,name) +% +% HITMAP produces horizontal and vertical cross section displaying the +% seismic ray count as hitmap. 3 default sections in the +% model are built (N-S, W-E, and horizontal). +% +% Input parameters: +% CoordGrid: 3D coordinates in matrix format +% rayCrossing: vector containing number of rays per cell +% +x = CoordGrid(:,1); +y = CoordGrid(:,2); +z = CoordGrid(:,3); +tbl = table(x,y,z,rayCrossing); + +% Build default hitmaps +myfig(); +h = heatmap(tbl,'x','y','ColorVariable','rayCrossing','Colormap', hot); +h.NodeChildren(3).YDir='normal'; +saveFigureAsImage([name '_H']); +close +myfig(); +vx = heatmap(tbl,'x','z','ColorVariable','rayCrossing','Colormap', hot); +vx.NodeChildren(3).YDir='normal'; +saveFigureAsImage([name '_WE']); +close +myfig(); +vy = heatmap(tbl,'y','z','ColorVariable','rayCrossing','Colormap', hot); +vy.NodeChildren(3).YDir='normal'; +saveFigureAsImage([name '_SN']); +close \ No newline at end of file diff --git a/bin/Murat_image3D.m b/bin/Murat_image3D.m index 887cf27..496578a 100644 --- a/bin/Murat_image3D.m +++ b/bin/Murat_image3D.m @@ -26,52 +26,51 @@ % Xp,Yp,Zp: interpolated matrices for parameter map close all -image = figure('Name',name,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); +% Create invisible figure +image = myfig(name); -stepgXYZ = [x(2)-x(1) y(2)-y(1) z(2)-z(1)]; +% Grid step and refined spacing +stepgXYZ = [diff(x(1:2)), diff(y(1:2)), diff(z(1:2))]; divix = stepgXYZ(1)/divi; diviy = stepgXYZ(2)/divi; diviz = stepgXYZ(3)/divi; -xp = x(1)-divix:divix:x(end)+divix; -yp = y(1)-diviy:diviy:y(end)+diviy; -zp = z(1)-diviz:diviz:z(end)+diviz; -zp = zp/1000; - -[Xp,Yp,Zp] = meshgrid(xp,yp,zp); -mVp = interp3(X,Y,Z,V,Xp,Yp,Zp); - -slice(Xp, Yp, Zp, mVp, sections(2), sections(1), sections(3)) - -scale_mVp(1) = max(mVp(:)); -scale_mVp(2) = abs(min(mVp(:))); -max_scale = max(scale_mVp); -if max_scale < 1 - max_scale = round(max_scale,2); - if max_scale == 0 - clim([-1 1]) - else - clim([-max_scale max_scale]) - end -elseif max_scale > 5 - max_scale = ceil(max_scale); - clim([-max_scale max_scale]) -end +% Build evenly spaced query vectors including one extra step on each end +nxp = round((x(end)-x(1))/divix) + 1; +nyp = round((y(end)-y(1))/diviy) + 1; +nzp = round((z(end)-z(1))/diviz) + 1; + +xp = linspace(x(1)-divix, x(end)+divix, nxp); +yp = linspace(y(1)-diviy, y(end)+diviy, nyp); +zp = linspace(z(1)-diviz, z(end)+diviz, nzp); +zp_km = zp / 1000; % convert to km for plotting + +% Create meshgrid for interpolation +[Xp,Yp,Zp] = meshgrid(xp,yp,zp_km); + +% Interpolate (explicit method and fill missing with NaN) +% Use 'linear' by default; change to 'natural' or 'cubic' if desired +mVp = interp3(X, Y, Z, V, Xp, Yp, Zp, 'linear', NaN); +% Plot slices (note: sections is [xslice, yslice, zslice] ordering used in original) +slice(Xp, Yp, Zp, mVp, sections(2), sections(1), sections(3)); % Z in km for plotting + +% Colormap and colorbar (single) colormap(color); -colorbar -shading flat -hcb = colorbar; -hcb.FontSize = 18; +hcb = colorbar; +hcb.FontSize = 18; + +shading flat; +hold on; -hold on +% Plot events (earthquakes and stations) scatter3(evestaz(:,2),evestaz(:,1),evestaz(:,3),60,'c',... 'MarkerEdgeColor','b', 'MarkerFaceColor',[.5 .5 .5], 'LineWidth',1) scatter3(evestaz(:,5),evestaz(:,4),evestaz(:,6),60,'^',... 'MarkerEdgeColor','m', 'MarkerFaceColor',[.5 .5 .5], 'LineWidth',1) +% Labels and ticks xlabel('Longitude (°)','FontSize',16,'FontWeight','bold','Color','k') ylabel('Latitude (°)','FontSize',16,'FontWeight','bold','Color','k') zlabel('Altitude (km)','FontSize',16,'FontWeight','bold','Color','k') @@ -80,12 +79,13 @@ ytick = linspace(y(1),y(end),6); ztick = linspace(z(end),z(1),6)/1000; -xticks(xtick); set(gca,'xticklabel',num2str(get(gca,'xtick')','%.2f')) -yticks(ytick); set(gca,'yticklabel',num2str(get(gca,'ytick')','%.2f')) -zticks(ztick); -set(gca,'zticklabel',num2str(get(gca,'ztick')','%.2f')) -axis tight +xticks(xtick); xticklabels(sprintfc('%.2f', get(gca,'xtick')')); +yticks(ytick); yticklabels(sprintfc('%.2f', get(gca,'ytick')')); +zticks(ztick); zticklabels(sprintfc('%.2f', get(gca,'ztick')')); + +axis tight; +set(gca, 'FontSize', 14); SetFDefaults(); -hold off +hold off; end diff --git a/bin/Murat_image3D_2panels.m b/bin/Murat_image3D_2panels.m index 23c4026..2ed9700 100644 --- a/bin/Murat_image3D_2panels.m +++ b/bin/Murat_image3D_2panels.m @@ -20,29 +20,26 @@ % Output parameters: % image: image produced in one panel -stepgXYZ = [x(2)-x(1) y(2)-y(1) z(2)-z(1)]; -divi = 5; -divix = stepgXYZ(1)/divi; -diviy = stepgXYZ(2)/divi; -diviz = stepgXYZ(3)/divi; +divi = 5; +dx = (x(2)-x(1))/divi; +dy = (y(2)-y(1))/divi; +dz = (z(2)-z(1))/divi; -xp = x(1)-divix:divix:x(end)+divix; -yp = y(1)-diviy:diviy:y(end)+diviy; -zp = z(1)-diviz:diviz:z(end)+diviz; -zp = zp/1000; +xp = x(1)-dx:dx:x(end)+dx; +yp = y(1)-dy:dy:y(end)+dy; +zp = (z(1)-dz:dz:z(end)+dz)/1000; -[Xp,Yp,Zp] = meshgrid(xp,yp,zp); -mVp = interp3(X,Y,Z,V,Xp,Yp,Zp); -z = sort(z)/1000; +[Xp,Yp,Zp] = meshgrid(xp,yp,zp); +mVp = interp3(X,Y,Z,V,Xp,Yp,Zp); +z = sort(z)/1000; -image =... - slice(Xp, Yp, Zp, mVp, sections(2), sections(1), sections(3)); +image =slice(Xp, Yp, Zp, mVp, sections(2), sections(1), sections(3)); colormap(color); colorbar shading flat -hcb = colorbar; -hcb.FontSize = 18; +hcb = colorbar; +hcb.FontSize= 18; hold on scatter3(evestaz(:,2),evestaz(:,1),evestaz(:,3),60,'c',... @@ -62,4 +59,4 @@ SetFDefaults(); hold off -end +end \ No newline at end of file diff --git a/bin/Murat_image3Dparameters.m b/bin/Murat_image3Dparameters.m index 5ece345..7c92ae3 100644 --- a/bin/Murat_image3Dparameters.m +++ b/bin/Murat_image3Dparameters.m @@ -1,4 +1,4 @@ -function image = ... +function image = ... Murat_image3Dparameters(X,Y,Z,V,color,sections,evestaz,x,y,z,name) % function image = ... % Murat_image3Dparameters(X,Y,Z,V,color,sections,evestaz,x,y,z,name) @@ -21,42 +21,41 @@ % Output parameters: % image: image produced close all -image = figure('Name',name,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); +image = myfig(name); -stepgXYZ = [x(2)-x(1) y(2)-y(1) z(2)-z(1)]; -divi = 1; -divix = stepgXYZ(1)/divi; -diviy = stepgXYZ(2)/divi; -diviz = stepgXYZ(3)/divi; +stepgXYZ = [x(2)-x(1) y(2)-y(1) z(2)-z(1)]; +divi = 1; +divix = stepgXYZ(1)/divi; +diviy = stepgXYZ(2)/divi; +diviz = stepgXYZ(3)/divi; -xp = x(1)-divix:divix:x(end)+divix; -yp = y(1)-diviy:diviy:y(end)+diviy; -zp = z(1)-diviz:diviz:z(end)+diviz; -zp = zp/1000; +xp = x(1)-divix:divix:x(end)+divix; +yp = y(1)-diviy:diviy:y(end)+diviy; +zp = z(1)-diviz:diviz:z(end)+diviz; +zp = zp/1000; -[Xp,Yp,Zp] = meshgrid(xp,yp,zp); -mVp = interp3(X,Y,Z,V,Xp,Yp,Zp); -z = sort(z)/1000; +[Xp,Yp,Zp] = meshgrid(xp,yp,zp); +mVp = interp3(X,Y,Z,V,Xp,Yp,Zp); +z = sort(z)/1000; slice(Xp, Yp, Zp, mVp, sections(2), sections(1), sections(3)) -scale_mVp(1) = max(mVp(:)); -scale_mVp(2) = abs(min(mVp(:))); -max_scale = max(scale_mVp); -if max_scale < 1 - max_scale = round(max_scale,2); +scale_mVp(1) = max(mVp(:)); +scale_mVp(2) = abs(min(mVp(:))); +max_scale = max(scale_mVp); +if max_scale < 1 + max_scale = round(max_scale,2); clim([-max_scale max_scale]) -elseif max_scale > 5 - max_scale = ceil(max_scale); +elseif max_scale > 5 + max_scale = ceil(max_scale); clim([-max_scale max_scale]) end colormap(color); colorbar shading flat -hcb = colorbar; -hcb.FontSize = 18; +hcb = colorbar; +hcb.FontSize = 18; hold on scatter3(evestaz(:,2),evestaz(:,1),evestaz(:,3),60,'c',... diff --git a/bin/Murat_imageCheckCN.m b/bin/Murat_imageCheckCN.m index f1588fa..2ca703a 100644 --- a/bin/Murat_imageCheckCN.m +++ b/bin/Murat_imageCheckCN.m @@ -1,57 +1,46 @@ -function CN_analysis = Murat_imageCheckCN(equationQ,residualQ_k,d1,... - spreadAverageQ,luntot_k,time0_k,energyRatio_k,A_k,Edirect_k,CN_title) -% function CN_analysis = Murat_imageCheckCN(equationQ,d1,... -% spreadAverageQ,luntot_k,time0_k,energyRatio_k,A_k,Edirect_k,CN_title) +function CN_analysis = Murat_imageCheckCN(time0_k,Q0,d0,Ed_k,CN_title) +% function CN_analysis = Murat_imageCheckCN(time0_k,Q0,d0,Ed_k,CN_title) % % PLOTS the Qc checks % % Input parameters: -% equationQ: sum of all three terms of the CN equation for average Q -% d1: data for average Q inversion -% spreadAverageQ:values of average spreading and Q for labels -% luntot_k: total length of ray % time0_k: travel time -% energyRatio_k: energy ratios -% A_k: CN inversion matrix -% Edirect_k: direct energy +% Q0: average Q with uncertainty +% d0: data for average Q inversion +% Ed_k: direct energy % CN_title: title of the figure % % Output parameters: % CN_analysis: figure for coda normalization check -CN_analysis = figure('Name',CN_title,'NumberTitle','off',... - 'Position',[20,400,1200,1000],'visible','off'); - -dRatio = log(energyRatio_k); -luntotQ = luntot_k/1000; -[U,S,~] = svd(A_k); +CN_analysis = myfig(CN_title); +equationQ = -time0_k*Q0(1); %% %Plot of the left and right hand sides of the CN equation. -subplot(3,1,1) -plot(time0_k,dRatio,'o','MarkerSize',6,'MarkerEdgeColor',[0 0 0]) +subplot(2,1,1) +plot(time0_k,d0,'o','MarkerSize',6,'MarkerEdgeColor',[0 0 0]) hold on -plot(time0_k,equationQ,'r*','MarkerSize',6) +plot(time0_k,equationQ,'r-') hold off xlabel('Travel time (s)','FontSize',10,'FontWeight','bold','Color','k') -ylabel('Log-energy ratio','FontSize',10,'FontWeight','bold','Color','k') -title(['Log-energy ratios vs travel time - \gamma = '... - num2str(spreadAverageQ(1,1)) '+/- ' ... - num2str(spreadAverageQ(1,2))]); -legend({'Q^{-1}',cat(2,' = ',num2str(1/spreadAverageQ(2,1)))},... +ylabel('Corrected log-energy ratio','FontSize',10,... + 'FontWeight','bold','Color','k') +title('Corrected log-energy ratio vs travel time'); +legend({'Q^{-1}',cat(2,' = ',num2str(Q0(1)),'+- ',num2str(Q0(2)))},... 'Location','northeast') SetFDefaults() %% %Plot of the direct energy versus distance. -subplot(3,1,2) -plot(log(luntotQ),log(Edirect_k),'o','MarkerSize',6,... +subplot(2,1,2) +plot(log(time0_k),log(Ed_k),'o','MarkerSize',6,... 'MarkerEdgeColor',[0 0 0]) -xlabel('Hypocentral distance (km)','FontSize',10,... +xlabel('Travel time (s)','FontSize',10,... 'FontWeight','bold','Color','k') ylabel('Log-energy (J/m^2)','FontSize',10,... 'FontWeight','bold','Color','k') -title('Log. direct energy vs hypocentral distance.'); +title('Log. direct energy vs travel time.'); xti = xticks; xt = cell(length(xti),1); @@ -59,10 +48,4 @@ xt(i,1) = {exp(xti(i))}; end xticklabels(xt) -SetFDefaults() -%% -% Then it checks the inversion with a Picard -subplot(3,1,3) -picard(U,diag(S),d1); -title(['Picard condition - Residual = ' num2str(residualQ_k)]); SetFDefaults() \ No newline at end of file diff --git a/bin/Murat_imageCheckPeakDelay.m b/bin/Murat_imageCheckPeakDelay.m index f22528b..1090cc7 100644 --- a/bin/Murat_imageCheckPeakDelay.m +++ b/bin/Murat_imageCheckPeakDelay.m @@ -1,4 +1,4 @@ -function pd_analysis = Murat_imageCheckPeakDelay(time0PD,... +function pd_analysis = Murat_imageCheckPeakDelay(time0PD,... fitrobust_k,peakData_k,sizeTitle,pd_title) % function pd_analysis = Murat_imageCheckPeakDelay(rtpdk,... % time0,fitrobust,peakData_k,sizeTitle,pd_title) @@ -15,24 +15,22 @@ % Output parameters: % pd_analysis: figure for peak delay check -pd_analysis = figure('Name',pd_title,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); +pd_analysis = myfig(pd_title); -log10Time = log10(time0PD); -fitrobust_i =... - fitrobust_k(1)*log10Time + fitrobust_k(2); +log10Time = log10(time0PD); +fitrobust_i = fitrobust_k(1)*log10Time + fitrobust_k(2); plot(log10Time,fitrobust_i,'r-',log10Time,log10(peakData_k),'ko') -xti = xticks; -xt = cell(length(xti),1); +xti = xticks; +xt = cell(length(xti),1); for i = 1:length(xti) - xt(i,1) = {10^xti(i)}; + xt(i,1) = {10^xti(i)}; end -yti = yticks; -yt = cell(length(yti),1); +yti = yticks; +yt = cell(length(yti),1); for i = 1:length(yticks) - yt(i,1) = {10^yti(i)}; + yt(i,1) = {10^yti(i)}; end %% % After removing outliers, it shows the best fit parameters for the diff --git a/bin/Murat_imageCheckQc.m b/bin/Murat_imageCheckQc.m index 621957d..14eb81d 100644 --- a/bin/Murat_imageCheckQc.m +++ b/bin/Murat_imageCheckQc.m @@ -1,5 +1,5 @@ -function Qc_analysis = Murat_imageCheckQc(Qm_k,RZZ_k,... - residualQc_k,luntot_Qc,Ac,sizeTitle,Qc_title,QcM) +function Qc_analysis = Murat_imageCheckQc(Qm_k,RZZ_k,... + residualQc_k,luntot_Qc,Ac,sizeTitle,Qc_title) % function Qc_analysis = Murat_imageCheckQc(Qm_k,RZZ_k,... % residualQc_k,luntot_Qc,Ac,sizeTitle,Qc_title,QcM) % @@ -12,19 +12,18 @@ % Ac: inverse coda attenuation matrix % sizeTitle: size of title font % Qc_title: title of Qc figure -% QcM: Linearized or Non Linear Qc measurement % % Output parameters: % Qc_analysis: figure for Qc check -Qc_analysis = figure('Name',Qc_title,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); +Qc_analysis = myfig(Qc_title); -mQm = mean(Qm_k); -Wc = Murat_weighting(RZZ_k,QcM); -Gc = Wc*Ac; -dck = Wc*Qm_k; -[Uc,Sc,~] = svd(Gc); +mQm = mean(Qm_k); +Wc = diag((1./RZZ_k).^2)/1e08; + +Gc = Wc*Ac; +dck = Wc*Qm_k; +[Uc,Sc,~] = svd(Gc); %% % It starts with Qc relative to the ray length in the first plot. subplot(2,1,1) @@ -34,15 +33,13 @@ title('Dependence of Qc^{-1} on ray lengths'); xlabel('Ray length (km)','FontSize',sizeTitle,... 'FontWeight','bold','Color','k') -ylabel('Qc^{-1}','FontSize',sizeTitle,... - 'FontWeight','bold','Color','k') -legend({'Qc^{-1}',cat(2,' = ',num2str(1/mQm))},... - 'Location','northeast') +ylabel('Qc^{-1}','FontSize',sizeTitle,'FontWeight','bold','Color','k') +legend({'Qc^{-1}',cat(2,' = ',num2str(1/mQm))},'Location','northeast') SetFDefaults() %% % A Picard plot evaluates the quality of the inversion. subplot(2,1,2) picard(Uc,diag(Sc),dck); -title(['Picard condition. Residual is: ' num2str(residualQc_k)]); +title(['Picard condition. Relative misfit reduction is: ' num2str(residualQc_k)]); SetFDefaults() \ No newline at end of file diff --git a/bin/Murat_imageDeclustering.m b/bin/Murat_imageDeclustering.m index 34c94a6..8659a25 100644 --- a/bin/Murat_imageDeclustering.m +++ b/bin/Murat_imageDeclustering.m @@ -1,4 +1,4 @@ -function clustering = Murat_imageDeclustering(locDegOriginal,... +function clustering = Murat_imageDeclustering(locDegOriginal,... locDegrees,origin,ending,name) % function clustering =... % Murat_imageDeclustering(locDegOriginal,locDegrees,origin,ending,name) @@ -15,8 +15,7 @@ % Output parameters: % clustering: image produced -clustering = figure('Name',name,'NumberTitle','off',... - 'Position',[20,400,1200,1000],'visible','off'); +clustering = myfig(name); plot([locDegOriginal(:,2),locDegOriginal(:,5)],... [locDegOriginal(:,1),locDegOriginal(:,4)],'-k','LineWidth',2) @@ -25,8 +24,8 @@ plot([locDegrees(:,2), locDegrees(:,5)],... [locDegrees(:,1),locDegrees(:,4)],'-r','LineWidth',2) -scatter(locDegOriginal(:,2),locDegOriginal(:,1),80,'c','MarkerEdgeColor','b',... - 'MarkerFaceColor',[.5 .5 .5], 'LineWidth',1) +scatter(locDegOriginal(:,2),locDegOriginal(:,1),80,'c',... + 'MarkerEdgeColor','b','MarkerFaceColor',[.5 .5 .5], 'LineWidth',1) scatter(locDegrees(:,5),locDegrees(:,4),80,'^','MarkerEdgeColor','m',... 'MarkerFaceColor',[.5 .5 .5], 'LineWidth',1) diff --git a/bin/Murat_imageKernels.m b/bin/Murat_imageKernels.m index 479b251..9cd13ff 100644 --- a/bin/Murat_imageKernels.m +++ b/bin/Murat_imageKernels.m @@ -16,9 +16,9 @@ function Murat_imageKernels(X,Y,Z,V,color,sections) shading flat; colormap(color); xlabel('Longitude (°)'); ylabel('Latitude (°)'); -zlabel('Altitude (m)'); +zlabel('Altitude (km)'); cH = colorbar; -ylabel(cH, 'Log-sensitivity'); +ylabel(cH, 'Differential Sensitivity'); set(cH,'FontSize',18); grid on ax = gca; diff --git a/bin/Murat_imageParameters.m b/bin/Murat_imageParameters.m index a29c116..b03213a 100644 --- a/bin/Murat_imageParameters.m +++ b/bin/Murat_imageParameters.m @@ -1,4 +1,4 @@ -function [image,para_condition,para_map]= ... +function [image,parCond,para_map] = ... Murat_imageParameters(x,y,z,modv_pd_k,modv_Qc_k,sizeTitle) % function [image,para_condition,para_map]= ... % Murat_imageParameters(x,y,z,modv_pd_k,modv_Qc_k,sizeTitle) @@ -17,65 +17,63 @@ % para_condition: condition on the parameters to image % para_map: parameter map -para_map = Murat_unfoldXYZ(x,y,z); -condition = abs(modv_pd_k(:,4))>10^(-10); -para_condition = para_map(condition,:); -pd_condition = modv_pd_k(condition,4); -Qc_condition = modv_Qc_k(condition,4); +para_map = Murat_unfoldXYZ(x,y,z); +condition = abs(modv_pd_k(:,4))>10^(-10); +parCond = para_map(condition,:); +pdCond = modv_pd_k(condition,4); +QcCond = modv_Qc_k(condition,4); -pdd =... - fitdist(pd_condition,'ExtremeValue'); +pdd = fitdist(pdCond,'ExtremeValue'); -pd_condition = pd_condition - pdd.mu; -trepd = 0.15*pdd.sigma; -mipdm = min(pd_condition); -mapdm = max(pd_condition); +pdCond = pdCond - pdd.mu; +trepd = 0.15*pdd.sigma; +mipdm = min(pdCond); +mapdm = max(pdCond); -Qcd =... - fitdist(Qc_condition,'GeneralizedExtremeValue'); -Qc_condition = Qc_condition - Qcd.mu; -treQc = 0.15*Qcd.sigma; -miQcm = min(Qc_condition); -maQcm = max(Qc_condition); +Qcd = fitdist(QcCond,'GeneralizedExtremeValue'); +QcCond = QcCond - Qcd.mu; +treQc = 0.15*Qcd.sigma; +miQcm = min(QcCond); +maQcm = max(QcCond); -image =... - figure('Name','Parameter space separation',... - 'NumberTitle','off','Position',[300,200,1200,1000],'visible','off'); +image =... + myfig('Parameter space separation'); -c =... - Qc_condition<-treQc & pd_condition<-trepd; -para_condition(c,4) = 1; -scatter(Qc_condition(c),pd_condition(c),65,'filled',... - 'MarkerFaceColor',[0 0.8 0]) +% Precompute masks (each logical vector computed once) +m1 = QcCond < -treQc & pdCond < -trepd; +m2 = QcCond < -treQc & pdCond > trepd; +m3 = QcCond > treQc & pdCond < -trepd; +m4 = QcCond > treQc & pdCond > trepd; +m5 = (QcCond>-treQc & QcCond-trepd & pdCondtrepd; -para_condition(c,4) = 2; -scatter(Qc_condition(c),pd_condition(c),65,'filled',... - 'MarkerFaceColor',[0 0.6 1]) +masks = {m1, m2, m3, m4, m5}; +vals = [1, 2, 3, 4, 0]; +sizes = [65, 65, 65, 65, 85]; +colors = [ 0 0.8 0; % green + 0 0.6 1; % cyan + 1 0.6 0; % orange + 1 0 0; % red + 0.7 0.7 0.7];% gray -c =... - Qc_condition>treQc & pd_condition<-trepd; -para_condition(c,4) = 3; -scatter(Qc_condition(c),pd_condition(c),65,'filled',... - 'MarkerFaceColor',[1 0.6 0]) - -c =... - Qc_condition>treQc & pd_condition>trepd; -para_condition(c,4) = 4; -scatter(Qc_condition(c),pd_condition(c),65,'filled',... - 'MarkerFaceColor',[1 0 0]) +% Apply parCond assignments and scatter plots in a short loop +hold on +for k = 1:numel(masks) + c = masks{k}; + if ~any(c), continue; end % skip empty groups + parCond(c,4) = vals(k); + if k < 5 + scatter(QcCond(c), pdCond(c), sizes(k), 'filled', ... + 'MarkerFaceColor', colors(k,:)); + else + scatter(QcCond(c), pdCond(c), sizes(k), 'filled', ... + 'MarkerFaceColor', colors(k,:), ... + 'MarkerEdgeColor', [1 1 1], 'LineWidth', 2); + end +end -c =... - (Qc_condition>-treQc & Qc_condition-trepd & pd_conditionspike_o(2) & r(:,1)spike_o(1) & r(:,2)spike_e(3); + r = Murat_unfoldXYZ(x,y,z); + spikeInput = r(:,1)>spike_o(2) & r(:,1)spike_o(1) & r(:,2)spike_e(3); +else + spikeInput = []; end end \ No newline at end of file diff --git a/bin/Murat_inversion.m b/bin/Murat_inversion.m index dcd01e5..21a7b4c 100644 --- a/bin/Murat_inversion.m +++ b/bin/Murat_inversion.m @@ -1,258 +1,283 @@ %% Peak-delay, Qc and Q TOMOGRAPHIC INVERSIONS -function Murat = Murat_inversion(Murat) -%% +function Murat = Murat_inversion(Murat) % Importing all the necessary inputs and data for plotting -FLabel = Murat.input.label; -outputLCurve = Murat.input.lCurve; -tWm = Murat.input.codaWindow; -cf = Murat.input.centralFrequency; -sped = Murat.input.spectralDecay; -sizea = Murat.input.sizeCheck; -latt = Murat.input.lowCheck; -hatt = Murat.input.highCheck; -modvP = Murat.input.modvPlot; -spike_o = Murat.input.spikeLocationOrigin; -spike_e = Murat.input.spikeLocationEnd; -spike_v = Murat.input.spikeValue; -x = Murat.input.x; -y = Murat.input.y; -z = Murat.input.z; -nxc = length(x); -nyc = length(y); -nzc = length(z); -nxyzc = nxc*nyc*nzc; -QcM = Murat.input.QcMeasurement; -inversionMethod = Murat.input.inversionMethod; -lCurveQc = Murat.input.lCurveQc; -lCurveQ = Murat.input.lCurveQ; -muratHeader = Murat.input.header; -Apd_i = Murat.PD.inversionMatrixPeakDelay; -Ac_i = Murat.Qc.inversionMatrixQc; -A_i = Murat.Q.inversionMatrixQ; -luntot = Murat.rays.totalLengthRay; -time0 = Murat.rays.travelTime; -Qm = Murat.Qc.inverseQc; -RZZ = Murat.Qc.uncertaintyQc; -lpdelta = Murat.PD.variationPeakDelay; -rapsp = Murat.Q.energyRatioBodyCoda; -retain_pd = Murat.PD.retainPeakDelay; -retain_Qc = Murat.Qc.retainQc; -retain_Q = Murat.Q.retainQ; -ray_crosses_pd = Murat.PD.raysPeakDelay; -ray_crosses_Qc = Murat.Qc.raysQc; -ray_crosses_Q = Murat.Q.raysQ; -tCoda = Murat.Qc.tCoda; -FPath = './'; - -lMF = size(ray_crosses_pd); -modv_pd = zeros(nxyzc,5,lMF(2)); -modv_Qc = zeros(nxyzc,10,lMF(2)); -modv_Q = zeros(nxyzc,10,lMF(2)); -const_Qc = zeros(size(rapsp)); -residualQ = zeros(1,lMF(2)); -residualQc = zeros(1,lMF(2)); -%% -% Loops over all frequencies and parameter models -for k = 1:lMF(2) - modv_pd(:,1:3,k) = modvP(:,1:3); - modv_Qc(:,1:3,k) = modvP(:,1:3); - modv_Q(:,1:3,k) = modvP(:,1:3); - cf_k = cf(k); - fcName = num2str(cf_k); - if find(fcName == '.') - fcName(fcName == '.') = '_'; - end - - %% - % Peak delay standard regionalization (for now) - rcpd_k = ray_crosses_pd(:,k); - rtpd_k = retain_pd(:,k); - Apd_k = Apd_i(rtpd_k,rcpd_k); - lpdelta_k = lpdelta(rtpd_k,k); - - A_boxes = Apd_k>0; - cts_box = sum(A_boxes,1); - mpd =... - sum(A_boxes.*lpdelta_k,1)'./sum(A_boxes,1)'; - - mpd(isnan(mpd)) = mean(mpd,'omitnan'); - modv_pd(rcpd_k,4,k) = mpd; - modv_pd(rcpd_k,5,k) = cts_box; - - %% - % Qc inversion - rcQc_k = ray_crosses_Qc(:,k); - rtQc_k = retain_Qc(:,k); - Ac_k = Ac_i(rtQc_k,rcQc_k); - Qm_k = Qm(rtQc_k,k); - RZZ_k = RZZ(rtQc_k,k); - Wc = Murat_weighting(RZZ_k,QcM); - Gc = Wc*Ac_k; - FName = ['L-curve_Qc_' fcName '_Hz']; - - bQm = Wc*Qm_k; - lCurveQc_k = lCurveQc(k); - - if isequal(inversionMethod,'Tikhonov') - [mtik0C,residualQc_k,LcQc,tik0_regC]... - =... - Murat_tikhonovQc(outputLCurve,Gc,bQm,lCurveQc_k); - - residualQc(1,k) = residualQc_k; - mQc = mtik0C; - - elseif isequal(inversionMethod,'Iterative') - disp(['Qc L-curve and cost functions at ', num2str(cf_k), ' Hz.']) - - [LcQc, minimizeVectorQm,infoVectorQm,tik0_regC]... - =... - Murat_minimiseCGLS(outputLCurve,Gc,bQm,lCurveQc_k,FName); - - residualQc(1,k) = min(infoVectorQm.Rnrm); - mQc = minimizeVectorQm; - - elseif isequal(inversionMethod,'Hybrid') - disp(['Qc cost function at ', num2str(cf_k), ' Hz.']) - - [LcQc, minimizeVectorQm,infoVectorQm,tik0_regC]... - =... - Murat_minimiseHybrid(outputLCurve,Gc,bQm,lCurveQc_k,FName); +s = Murat.input; % local shorthand + +% Inputs +FLabel = s.label; +tWm = s.codaWindow; +cf = s.centralFrequency; +sizea = s.sizeCheck; +modvP = s.modvPlot; +spike_o = s.spikeLocationOrigin; +spike_e = s.spikeLocationEnd; +spike_v = s.spikeValue; +x = s.x; +y = s.y; +z = s.z; +nxc = numel(x); +nyc = numel(y); +nzc = numel(z); +nxyzc = nxc*nyc*nzc; +inversionMethod = s.inversionMethod; +DDcoordinates = s.DDcoordinates; +B0 = s.albedo; +Le_1 = s.iExtinctionLength; +vS = s.averageVelocityS; +iter = s.MaximumIterations; +iterStall = s.MaximumStallIterations; +dampValueQc = s.dampingValueQc; +smoothValueQc = s.smoothingValueQc; +dampValueQ = s.dampingValueQ; +smoothValueQ = s.smoothingValueQ; +plotI = s.PlotInversion; + +% Matrices / data from Murat struct +Apd_i = Murat.PD.inversionMatrixPeakDelay; +Ac_i = Murat.Qc.inversionMatrixQc; +A_i = Murat.Q.inversionMatrixQ; +luntot = Murat.rays.totalLengthRay; +time0 = Murat.rays.travelTime; +Qm = Murat.Qc.inverseQc; +RZZ = Murat.Qc.uncertaintyQc; +lpdelta = Murat.PD.variationPeakDelay; +rapE = Murat.Q.energyRatioBodyCoda; +retainPD = Murat.PD.retainPeakDelay; +retainQc = Murat.Qc.retainQc; +retainQ = Murat.Q.retainQ; +rayCrossesPD = Murat.PD.raysPeakDelay; +rayCrossesQc = Murat.Qc.raysQc; +rayCrossesQ = Murat.Q.raysQ; +tCoda = Murat.Qc.tCoda; +outputSolverQc = struct; +outputSolverQ = struct; +exitFlagSolverQc = struct; +exitFlagSolverQ = struct; + +% Paths and outputs +FPath = './'; +outDirTXT = fullfile(FPath, FLabel, 'TXT'); +lMF = size(rayCrossesPD); +nFreq = lMF(2); + +% Preallocate +modvPD = zeros(nxyzc,5,nFreq); +modvQc = zeros(nxyzc,10,nFreq); +modvQ = zeros(nxyzc,10,nFreq); +residualQ = zeros(1,nFreq); +residualQc = zeros(1,nFreq); +dValueQc = zeros(1,nFreq); +dValueQ = zeros(1,nFreq); + +% Diffusion constant - Wu 1985 +D = vS./Le_1/3./(1-B0); + +fc_names = cell(1,nFreq); +fld_names = cell(1,nFreq); +outDirFigureBase = fullfile(FPath,FLabel,'Tests','LCurve'); +for k = 1:nFreq + cf_k = cf(k); + fc_names{k} = strrep(num2str(cf_k),'.','_'); + tmp = sprintf('Hz%g', cf_k); + fld_names{k} = strrep(tmp,'.','_'); +end - residualQc(1,k) = min(infoVectorQm.Rnrm); - mQc = minimizeVectorQm; +% reuse buffers to avoid reallocations +siz = [nxc nyc nzc]; +I = []; checkInput = []; spikeInput = []; - else - error('Unknown inversion method.') +% Precompute coordinate block once and reuse +coords_block = modvP(:,1:3); +% Loops over all frequencies and parameter models +for k = 1:nFreq + + D_k = D(k); + if ~isempty(dampValueQc), dValueQc_k = dampValueQc(k); + else, dValueQc_k = []; end + + if ~isempty(dampValueQ), dValueQ_k = dampValueQ(k); + else, dValueQ_k = []; end + + if ~isempty(smoothValueQc), sValueQc_k= smoothValueQc(k); + else, sValueQc_k = []; end + + if ~isempty(smoothValueQ), sValueQ_k = smoothValueQ(k); + else, sValueQ_k = []; end + + modvPD(:,1:3,k) = coords_block; + modvQc(:,1:3,k) = coords_block; + modvQ(:,1:3,k) = coords_block; + cf_k = cf(k); + fcName = fc_names{k}; + fld = fld_names{k}; + outDirFigure = fullfile(outDirFigureBase,['L-curve_Qc_' fcName '_Hz']); + + % --- Peak delay standard regionalization --- + rcpd_k = rayCrossesPD(:,k); + rtpd_k = retainPD(:,k); + Apd_k = Apd_i(rtpd_k,rcpd_k); + lpdelta_k = lpdelta(rtpd_k,k); + + A_boxes = Apd_k>0; + counts_box = sum(A_boxes,1); + mpd = sum(A_boxes.*lpdelta_k,1)'./counts_box'; + + mpd(isnan(mpd)) = mean(mpd,'omitnan'); + modvPD(rcpd_k,4,k) = mpd; + modvPD(rcpd_k,5,k) = counts_box; + + % --- Qc inversion --- + rcQc_k = rayCrossesQc(:,k); + rtQc_k = retainQc(:,k); + Ac_k = Ac_i(rtQc_k,rcQc_k,k); + RZZ_k = RZZ(rtQc_k,k); + Qm_k = Qm(rtQc_k,k); + coordPriorQc = modvQc(rcQc_k,1:3,k); + + [sol,fval,exitflag,output,dValueQc_k]= Murat_inversionQc(Ac_k,Qm_k,... + inversionMethod,iter,iterStall,RZZ_k,coordPriorQc,dValueQc_k,... + sValueQc_k,plotI,outDirFigure); + + modvQc(rcQc_k,4,k) = sol.Qc; + residualQc(k) = fval; + fprintf(['Qc relative misfit reduction at ' num2str(cf_k)... + 'Hz is: %.4f\n'], fval); + exitFlagSolverQc.(fld) = exitflag; + outputSolverQc.(fld) = output; + + % --- Q inversion --- + rcQ_k = rayCrossesQ(:,k); + rtQ_k = retainQ(:,k); + keepMask = rtQ_k & rtQc_k; + A_k = A_i(keepMask,rcQ_k); + Qc_k = Qm(keepMask,k); + luntot_k = luntot(keepMask); + l = luntot_k/1000; + time0_k = time0(keepMask); + rapE_k = rapE(keepMask,k); + tCm = tCoda(keepMask,k); + coordPriorQ = modvQ(rcQ_k,1:3,k); + + [d0, Q0,const_Qc_k] = Murat_lsqlinQmean(tCm,tWm,Qc_k,cf_k,D_k,l,... + time0_k,rapE_k); + + outDirFigure = fullfile(outDirFigureBase, ['L-curve_Q_' fcName '_Hz']); + + [sol,fval,exitflag,output,dValueQ_k] = Murat_inversionQ(A_k,d0,... + rapE_k,inversionMethod,Q0,iter,iterStall,coordPriorQ,dValueQ_k,... + sValueQ_k,plotI,outDirFigure); + + modvQ(rcQ_k,4,k) = sol.Q; + residualQ(k) = fval; + fprintf(['Q relative misfit reduction at ' num2str(cf_k)... + 'Hz is: %.4f\n'], fval); + exitFlagSolverQ.(fld) = exitflag; + outputSolverQ.(fld) = output; + + % --- Checkerboards and spike inputs and checkerboard inversion --- + % --- Qc --- + if isempty(I) + I = checkerBoard3D(siz, sizea); + [checkInput, spikeInput] = Murat_inputTesting(I, spike_o,... + spike_e, x, y, z); end - modv_Qc(rcQc_k,4,k) = mQc; - - saveas(LcQc,fullfile(FPath, FLabel,'Tests/LCurve',FName)); - saveas(LcQc,fullfile(FPath, FLabel,'Tests/LCurve',FName),'png'); - close(LcQc) - - %% - % Q inversion - rcQ_k = ray_crosses_Q(:,k); - rtQ_k = retain_Q(:,k); - A_k = A_i(rtQ_k & rtQc_k,rcQ_k); - Q_k = Qm(rtQ_k & rtQc_k,k); - luntot_k = luntot(rtQ_k & rtQc_k); - time0_k = time0(rtQ_k & rtQc_k); - rapsp_k = rapsp(rtQ_k & rtQc_k,k); - tCm = tCoda(rtQ_k & rtQc_k,k); - - [d1, const_Qc_k, ~, ~] = Murat_lsqlinQmean(tCm,tWm,Q_k,... - cf_k,sped,luntot_k,time0_k,rapsp_k); - const_Qc(rtQ_k & rtQc_k,k) = const_Qc_k; - - lCurveQ_k = lCurveQ(k); - FName = ['L-curve_Q_' fcName '_Hz']; - if isequal(inversionMethod,'Tikhonov') - [mtik0,residualQ_k,LcCN,tik0_reg]... - =... - Murat_tikhonovQ(outputLCurve,A_k,d1,lCurveQ_k,1); - - residualQ(:,k) = residualQ_k; - mQ = mtik0; - - elseif isequal(inversionMethod,'Iterative') - disp(['Q L-curve and cost functions at ', num2str(cf_k), ' Hz.']) - [LcCN, minimizeVectorQ,infoVectorQ,tik0_reg]... - =... - Murat_minimiseCGLS(outputLCurve,A_k,d1,lCurveQ_k,FName); - - residualQ(1,k) = min(infoVectorQ.Rnrm); - mQ = minimizeVectorQ; - - elseif isequal(inversionMethod,'Hybrid') - disp(['Q cost function at ', num2str(cf_k), ' Hz.']) - - [LcCN, minimizeVectorQ,infoVectorQ,tik0_reg]... - =... - Murat_minimiseHybrid(outputLCurve,A_k,d1,lCurveQ_k,FName); - - residualQ(1,k) = min(infoVectorQ.Rnrm); - mQ = minimizeVectorQ; - + + Qm_k_all = Qm(rtQc_k,k); % local + + modvQc(checkInput==1,6,k) = min(Qm_k_all); + modvQc(checkInput==0,6,k) = max(Qm_k_all); + modvQc(:,8,k) = mean(Qm_k_all); + if ~isempty(spikeInput) + modvQc(spikeInput,8,k) = spike_v; end - modv_Q(rcQ_k,4,k) = mQ; - - saveas(LcCN,fullfile(FPath, FLabel,'Tests/LCurve',FName)); - saveas(LcCN,fullfile(FPath, FLabel,'Tests/LCurve',FName),'png'); - close(LcCN) - - %% Checkerboards and spike inputs and checkerboard inversion - % Qc - siz = [nxc nyc nzc]; - I = checkerBoard3D(siz,sizea); - [checkInput,spikeInput] =... - Murat_inputTesting(I,spike_o,spike_e,x,y,z); - - modv_Qc(checkInput==1,6,k) = latt; - modv_Qc(checkInput==0,6,k) = hatt; - modv_Qc(:,8,k) = mean(Qm_k); - modv_Qc(spikeInput,8,k) = spike_v; - Qc_ch = modv_Qc(rcQc_k,6,k); - re_checkQc = Gc*Qc_ch; - - modv_Qc(rcQc_k,7,k) =... - Murat_outputTesting(Gc,re_checkQc,tik0_regC,inversionMethod); - %% - % Q - modv_Q(:,6:8,k) = modv_Qc(:,6:8,k); - Q_ch = modv_Q(rcQ_k,6,k); - re_Q = A_k*Q_ch; + Qc_ch = modvQc(rcQc_k,6,k); + re_checkQc = Ac_k*Qc_ch; + [sol,~,~,~,~] = Murat_inversionQc(Ac_k,re_checkQc,... + inversionMethod,iter,iterStall,RZZ_k,coordPriorQc,... + dValueQc_k,sValueQc_k,plotI,outDirFigure); + modvQc(rcQc_k,7,k) = sol.Qc; + + % --- Q: reuse Qc checkerboard parameters --- + modvQ(:,6:8,k) = modvQc(:,6:8,k); + Q_ch = modvQ(rcQ_k,6,k); + re_checkQ = A_k*Q_ch; + + % Synthetic energy ratios + synthEratio = exp(const_Qc_k + 2*pi*cf_k*re_checkQ)*2*pi*cf_k... + ./l.^2; - modv_Q(rcQ_k,7,k) =... - Murat_outputTesting(A_k,re_Q,tik0_reg,inversionMethod); + [d0c, Q0c, ~] = Murat_lsqlinQmean(tCm,tWm,Qc_k,cf_k,D_k,l,... + time0_k,synthEratio); + + [sol,~,~,~,~] = Murat_inversionQ(A_k,d0c,rapE_k,... + inversionMethod,Q0c,iter,iterStall,coordPriorQ,dValueQ_k,... + sValueQ_k,plotI,outDirFigure); + + modvQ(rcQ_k,7,k)= sol.Q; - %% - % Inverting spike for Qc and Q at user discretion + % --- Spike inversion if requested --- if ~isempty(spike_o) - Qc_sp = modv_Qc(rcQc_k,8,k); - re_spikeQc = Gc*Qc_sp; - - modv_Qc(rcQc_k,9,k) =... - Murat_outputTesting(Gc,re_spikeQc,tik0_regC,inversionMethod); - - Q_sp = modv_Q(rcQ_k,8,k); - re_spikeQ = A_k*Q_sp; + Qc_sp = modvQc(rcQc_k,8,k); + re_spikeQc = Ac_k*Qc_sp; + [sol,~,~,~,~] = Murat_inversionQc(Ac_k,re_spikeQc,... + inversionMethod,iter,iterStall,RZZ_k,coordPriorQc,... + dValueQc_k,sValueQc_k,plotI,outDirFigure); + modvQc(rcQc_k,9,k) = sol.Qc; + + Q_sp = modvQ(rcQ_k,8,k); + re_spikeQ = A_k*Q_sp; + + %Synthetic energy ratios + synthEratio = exp(const_Qc_k + 2*pi*cf_k*re_spikeQ)*... + 2*pi*cf_k./l.^2; + + [d0s, Q0s, ~] = Murat_lsqlinQmean(tCm,tWm,Qc_k,cf_k,D_k,l,... + time0_k,synthEratio); + + [sol,~,~,~,~] = Murat_inversionQ(A_k,d0s,rapE_k,... + inversionMethod,Q0s,iter,iterStall,coordPriorQ,dValueQ_k,... + sValueQ_k,plotI,outDirFigure); - modv_Q(rcQ_k,9,k) =... - Murat_outputTesting(A_k,re_spikeQ,tik0_reg,inversionMethod); + modvQ(rcQ_k,9,k)= sol.Q; end - %% - % Save peak-delay, Qc, Q - DDcoordinates = Murat.input.DDcoordinates; - modv_pd_dd = modv_pd(:,:,k); - modv_pd_dd(:,1:3) = DDcoordinates; - modv_Qc_dd = modv_Qc(:,:,k); - modv_Qc_dd(:,1:3) = DDcoordinates; - modv_Q_dd = modv_Q(:,:,k); - modv_Q_dd(:,1:3) = DDcoordinates; + % --- Save outputs for this frequency --- + modv_pd_dd = modvPD(:,:,k); + modv_pd_dd(:,1:3) = DDcoordinates; + modv_Qc_dd = modvQc(:,:,k); + modv_Qc_dd(:,1:3) = DDcoordinates; + modv_Q_dd = modvQ(:,:,k); + modv_Q_dd(:,1:3) = DDcoordinates; + dValueQc(k) = dValueQc_k; + dValueQ(k) = dValueQ_k; + + writematrix(modv_pd_dd,... + fullfile(outDirTXT, ['peakdelay_' fcName '_Degrees_Hz.txt'])); + writematrix(modv_Qc_dd,... + fullfile(outDirTXT, ['Qc_' fcName '_Degrees_Hz.txt'])); + writematrix(modv_Q_dd,... + fullfile(outDirTXT, ['Q_' fcName '_Degrees_Hz.txt'])); +end - FName =... - ['peakdelay_' fcName '_Degrees_Hz.txt']; - writematrix(modv_pd_dd,fullfile(FPath, FLabel, 'TXT', FName)); +% Save back to Murat +Murat.input.DiffConstant = D; - FName = ['Qc_' fcName '_Degrees_Hz.txt']; - writematrix(modv_Qc_dd,fullfile(FPath, FLabel, 'TXT', FName)); +Murat.PD.modvPeakDelay = modvPD; - FName = ['Q_' fcName '_Degrees_Hz.txt']; - writematrix(modv_Q_dd,fullfile(FPath, FLabel, 'TXT', FName)); -end +Murat.Qc.modvQc = modvQc; +Murat.Qc.residualRedQc = residualQc; +Murat.Qc.dampingValueQc = dValueQc; +Murat.Qc.outputSolverQc = outputSolverQc; +Murat.Qc.exitFlagSolverQc = exitFlagSolverQc; + +Murat.Q.residualRedQ = residualQ; +Murat.Q.dampingValueQ = dValueQ; +Murat.Q.modvQ = modvQ; +Murat.Q.outputSolverQ = outputSolverQ; +Murat.Q.exitFlagSolverQ = exitFlagSolverQ; -%% -% Save in Murat -Murat.Qc.residualQc = residualQc; -Murat.Q.const_Qc = const_Qc; -Murat.Q.residualQ = residualQ; -Murat.PD.modvPeakDelay = modv_pd; -Murat.Qc.modvQc = modv_Qc; -Murat.Q.modvQ = modv_Q; -writetable(muratHeader,fullfile(FPath, FLabel, 'TXT', 'DataHeaders.xlsx')); +end \ No newline at end of file diff --git a/bin/Murat_inversionQ.m b/bin/Murat_inversionQ.m new file mode 100644 index 0000000..d1d8b6a --- /dev/null +++ b/bin/Murat_inversionQ.m @@ -0,0 +1,123 @@ +function [sol,fval,exitflag,output,dampValue]= Murat_inversionQ(A_k,d0,... + rapsp_k,inversionMethod,Q0,iter,iterStall,coordPrior,dampValue,... + smoothValue,PlotI,pathFolder) +% function [sol,fval,exitflag,output,dampValue]=Murat_inversionQ(A_k,d0,... +% inversionMethod,Q0,iter,iterStall,coordPrior,dampValue,... +% smoothValue,PlotI,pathFolder) +% +% INVERTS spatial values of Q using ray approximation +% +% Input parameters: +% A_k: linearised kernel from ray approximation +% d0: data out of average Q computation +% inversionMethod: choose between three optimizations +% Q0: average Q with variance +% iter: iterations for the non-linear optimization +% iterStall: max iterations for the non-linear optimization +% coordPrior: coords for the starting solution +% dampValue: damping value +% smoothValue: smoothing value +% PlotI: plotting iterations during inversion +% pathFolder: folder to save inversion figure for Tikhonov +% +% Output parameters: +% [sol,fval,exitflag,output]: standard optimization outputs + +%% +%Parameter model number +MQ = size(A_k,2); + +%Starting model from regionalization +d = d0./sum(A_k,2); +Ac_boxes = abs(A_k./sum(A_k,1)); +m0Q = zeros(MQ,1); +for i = 1:MQ + n = Ac_boxes(:,i); + m = n(n>0); + d1 = d(n>0); + m0Q(i) = sum(m.*d1); +end + +if smoothValue~=0 + %Variance for models to square of 1/10 of the mean inverse Qc value + sigmaM = Q0(1); + CovM = Murat_smoothing(coordPrior,smoothValue,sigmaM); +else + CovM = 0; +end + + +if ~isempty(dampValue) + dampValue = abs(dampValue); +else + %Damping is the mean energy ratio + dampValue = 0.1*mean(rapsp_k); +end + +Q = optimvar('Q',MQ,LowerBound=10^-7); +x0Q.Q = Q0(1)*ones(MQ,1); +obj0 = norm(d0 - A_k*m0Q)^2 + ... + dampValue*norm(m0Q)^2 +... + norm(CovM*m0Q); + +objQ = norm(d0 - A_k*Q)^2 +... + dampValue*norm(Q)^2 + ... + norm(CovM*Q); + +probQ = optimproblem('Objective',objQ); +maxStall= 'MaxStallIterations'; +maxIt = 'MaxIterations'; + +switch inversionMethod + + case 'Tikhonov' + [sol.Q,fval,~] = Murat_tikhonovQ(PlotI,A_k,d0,dampValue,m0Q); + sol.Q = sol.Q; + exitflag = 'Tikhonov'; + output = []; + saveFigureAsImage(pathFolder); + savefig(gcf, [pathFolder '.fig']); + close(gcf); + fval = fval*obj0; + + case 'Particle' + if PlotI == 1 + options = optimoptions(@particleswarm,maxStall,... + iterStall,maxIt,iter,'PlotFcn','pswplotbestf'); + else + options = optimoptions(@particleswarm,maxStall,... + iterStall,maxIt,iter); + + end + [sol,fval,exitflag,output] = solve(probQ,x0Q,'Solver',... + 'particleswarm','Options',options); + + case 'Annealing' + if PlotI == 1 + options = optimoptions(@simulannealbnd,maxStall,... + iterStall,maxIt,iter,'PlotFcns',... + {@saplotbestx,@saplotbestf,@saplotx,@saplotf}); + else + options = optimoptions(@simulannealbnd,maxStall,... + iterStall,maxIt,iter); + + end + [sol,fval,exitflag,output] = solve(probQ,x0Q,"Solver",... + "simulannealbnd",'Options',options); + + case 'Global' + if PlotI == 1 + options = optimoptions(@ga,'PlotFcn','gaplotbestf'); + [sol,fval,exitflag,output] = solve(probQ, "Solver",... + "ga",'Options',options); + else + [sol,fval,exitflag,output] = solve(probQ, "Solver","ga"); + + end + + otherwise + error('Unknown inversion method.') +end + +fval = fval/obj0; +end \ No newline at end of file diff --git a/bin/Murat_inversionQc.m b/bin/Murat_inversionQc.m new file mode 100644 index 0000000..9843dce --- /dev/null +++ b/bin/Murat_inversionQc.m @@ -0,0 +1,122 @@ +function [sol,fval,eflag,output,dampValue] = Murat_inversionQc(Ac_k,... + Qm_k,inversionMethod,iter,iterStall,RZZ_k,coordPrior,dampValue,... + smoothValue,PlotI,pathFolder) +% function [sol,fval,eflag,output]=Murat_inversionQc(Ac_k,Qm_k,inversionMethod) +% +% INVERTS spatial values of Qc using pre-compiled kernels +% +% Input parameters: +% Ac_k: differential sensitivity kernel +% Qm_k: measured coda Q +% inversionMethod: choose between three optimizations +% iter: iterations for the non-linear optimization +% iterStall: max iterations for the non-linear optimization +% RZZ_k: std to weight inversion matrix +% coordPrior: coords for the starting solution +% dampValue: damping value +% smoothValue: smoothing value +% PlotI: plotting iterations during inversion +% pathFolder: folder to save inversion figure for Tikhonov +% +% Output parameters: +% [sol,fval,exitflag,output]: standard optimization outputs + +%% +%Parameter model number +MQc = size(Ac_k,2); + +%Covariance matrix for the data +w = 1 ./ RZZ_k; +%w = (w - min(w(:))) ./ (max(w(:)) - min(w(:))); +W = diag(w.^2); + + +%Starting model from regionalization +Ac_boxes = sum(Ac_k,1); +m0Qc = (sum(Ac_k.*Qm_k,1)')./Ac_boxes'; + +if smoothValue~=0 + sigmaM = mean(m0Qc/10)^2; + CovM = Murat_smoothing(coordPrior,smoothValue,sigmaM); +else + CovM = 0; +end + +if ~isempty(dampValue) + dampValue = abs(dampValue); +else + %Damping is also 1/10 of the mean inverse Qc value + dampValue = mean(m0Qc/10); +end + + +% --- optimization variables and objective --- +Qc = optimvar('Qc',MQc,'LowerBound',min(Qm_k)); +x0.Qc = m0Qc; + +obj0 = norm(W*(Qm_k - Ac_k*m0Qc))^2 + ... + dampValue*norm(m0Qc)^2 +... + norm(CovM*m0Qc); + +obj = norm(W*(Qm_k - Ac_k*Qc))^2 + ... + dampValue*norm(Qc)^2 +... + norm(CovM*Qc); + +prob = optimproblem('Objective',obj); +maxStall = 'MaxStallIterations'; +maxIt = 'MaxIterations'; +switch inversionMethod + + case 'Tikhonov' + [sol.Qc,fval,~] =... + Murat_tikhonovQc(PlotI,W*Ac_k,W*Qm_k,dampValue,x0.Qc); + eflag = 'Tikhonov'; + output = []; + saveFigureAsImage(pathFolder); + savefig(gcf, [pathFolder '.fig']); + close(gcf); + fval = fval*obj0; + + case 'Particle' + if PlotI == 1 + options = optimoptions(@particleswarm,maxStall,... + iterStall,maxIt,iter,'PlotFcn','pswplotbestf'); + + else + options = optimoptions(@particleswarm,maxStall,... + iterStall,maxIt,iter); + + end + [sol,fval,eflag,output] = solve(prob,x0,'Solver',... + 'particleswarm','Options',options); + + + case 'Annealing' + if PlotI == 1 + options = optimoptions(@simulannealbnd,maxStall,... + iterStall,maxIt,iter,'PlotFcns',... + {@saplotbestx,@saplotbestf,@saplotx,@saplotf}); + else + options = optimoptions(@simulannealbnd,maxStall,... + iterStall,maxIt,iter); + + end + [sol,fval,eflag,output] = solve(prob,x0,"Solver",... + "simulannealbnd",'Options',options); + + case 'Global' + if PlotI == 1 + options = optimoptions(@ga,'PlotFcn','gaplotbestf'); + [sol,fval,eflag,output] = solve(prob, "Solver",... + "ga",'Options',options); + else + [sol,fval,~,output] = solve(prob, "Solver","ga"); + + end + + otherwise + error('Unknown inversion method.') +end + +fval = fval/obj0; +end \ No newline at end of file diff --git a/bin/Murat_kernels.m b/bin/Murat_kernels.m index 1d19898..4452748 100644 --- a/bin/Murat_kernels.m +++ b/bin/Murat_kernels.m @@ -49,45 +49,45 @@ % Original creators: Angel De La Torre & Edoardo Del Pezzo % Adapted for coda attenuation tomography by De Siena. -DT = 0.5; +DT = 0.5; +lR = numel(B0); %% -% Initial computations to understand were to compute in space -x1 = unique(modv(:,1)); -y1 = unique(modv(:,2)); -z1 = sort(unique(modv(:,3)),'descend'); -stepgx = x1(2)-x1(1); -stepgy = y1(2)-y1(1); -stepgz = z1(2)-z1(1); - -DRx = stepgx/kT; -DRy = stepgy/kT; -DRz = stepgz/kT; -DR = min([DRx DRy abs(DRz)])/1000; +% Initial computations to understand where to compute in space +x1 = unique(modv(:,1)); +y1 = unique(modv(:,2)); +z1 = sort(unique(modv(:,3)),'descend'); +stepgx = x1(2)-x1(1); +stepgy = y1(2)-y1(1); +stepgz = z1(2)-z1(1); + +DRx = stepgx/kT; +DRy = stepgy/kT; +DRz = stepgz/kT; +DR = min([DRx DRy abs(DRz)])/1000; % Origin of the grid: middle point between source and receiver -origin = (event(1:3)+station(1:3))/2; +origin = (event(1:3)+station(1:3))/2; % Add a cushion of 5 seconds -S = (v*T+5)*1000; +S = (v*T+5)*1000; % New reference system -xyzs = event(1:3)-origin(1:3); -xyzr = station(1:3)-origin(1:3); +xyzs = event(1:3)-origin(1:3); +xyzr = station(1:3)-origin(1:3); % Distance between source and receiver -dxyz = xyzr(1:3)-xyzs(1:3); -D0 =... - sqrt(dxyz(1)^2 + dxyz(2)^2 + dxyz(3)^2)/1000; +dxyz = xyzr(1:3)-xyzs(1:3); +D0 = sqrt(dxyz(1)^2 + dxyz(2)^2 + dxyz(3)^2)/1000; % Grid definition -x_grid = -S+DRx:DRx:S; -y_grid = -S+DRy:DRy:S; -z_grid = max(z1)-origin(3):DRz:-S; +x_grid = -S+DRx:DRx:S; +y_grid = -S+DRy:DRy:S; +z_grid = max(z1)-origin(3):DRz:-S; -Nx = length(x_grid); -Ny = length(y_grid); -Nz = length(z_grid); -Nxyz = Nx*Ny*Nz; +Nx = numel(x_grid); +Ny = numel(y_grid); +Nz = numel(z_grid); +Nxyz = Nx*Ny*Nz; if Nxyz>50e6 fprintf('Warning: a lot of points are going to be included in the grid (%d points)\n',Nx*Ny*Nz) @@ -96,114 +96,121 @@ end % Sets the 3D matrix -r_grid = Murat_unfoldXYZ(x_grid',y_grid',z_grid'); +r_grid = Murat_unfoldXYZ(x_grid',y_grid',z_grid'); % Distances r1 and r2, from each point of the grid to source or to receptor -xyz = r_grid(:,1:3); -d1xyz = xyz-xyzs; -d2xyz = xyz-xyzr; -r1_grid =... - sqrt(d1xyz(:,1).^2 + d1xyz(:,2).^2 + d1xyz(:,3).^2)/1000; -r2_grid =... - sqrt(d2xyz(:,1).^2 + d2xyz(:,2).^2 + d2xyz(:,3).^2)/1000; +xyz = r_grid(:,1:3); +d1xyz = xyz-xyzs; +d2xyz = xyz-xyzr; +r1_grid = sqrt(d1xyz(:,1).^2 + d1xyz(:,2).^2 + d1xyz(:,3).^2)/1000; +r2_grid = sqrt(d2xyz(:,1).^2 + d2xyz(:,2).^2 + d2xyz(:,3).^2)/1000; % Elements of the grid inside the ellipsoid if isequal(lapseTimeMethod,'Peak') - T = mean(T); + T = mean(T); end -cond_interior = r1_grid + r2_grid < T*v; +cond_interior = r1_grid + r2_grid < T*v; %% % Catalog of Paasschens Functions % Vector of distances -R = DR:DR:T*v; +R = DR:DR:T*v; % Max number points coda -NT = ceil((T+DT)/DT); +NT = ceil((T+DT)/DT); % Number of distances -NR = length(R); +NR = length(R); % To store t0 for each r -t0_R = zeros(NR,1); +t0_R = zeros(NR,1); % To store A for each r -A_R = zeros(NR,1); +A_R = zeros(NR,lR); % Store the number of points in coda for each r -N_R = zeros(NR,1); +N_R = zeros(NR,1); % Store the coda for each r -coda_R = zeros(NR,NT); - -for idx_r=1:NR - r = R(idx_r); - [t0,A,N,coda] =... - Murat_paasschensFunction(r,v,B0,Le_1,DT,T); - t0_R(idx_r) = t0; - A_R(idx_r) = A; - N_R(idx_r) = N; - coda_R(idx_r,1:N) = coda(1:N); +coda_R = zeros(NT,lR,NR); + +for idx_r = 1:NR + r = R(idx_r); + [t0,A,N,coda] = Murat_paasschensFunction(r,v,B0,Le_1,DT,T); + t0_R(idx_r) = t0; + A_R(idx_r,:) = A; + N_R(idx_r) = N; + coda_R(1:N,:,idx_r) = coda(1:N,:); end %% % Catalog of r1 - r2 convolutions evaluated at T -R1 = R; -R2 = R; -coda_coda = zeros(NR,NR); -coda_delta = zeros(NR,NR); - -for idx_r1=1:NR - for idx_r2 = 1:idx_r1 - r1 = R1(idx_r1); - r2 = R2(idx_r2); +coda_coda = zeros(NR,NR,lR); +coda_delta = zeros(NR,NR,lR); + +% Precompute some vectors for speed +t0_vec = t0_R; +A_vec = A_R; +codaM = coda_R; + +for idx_r1 = 1:NR + r1 = R(idx_r1); + t0_1 = t0_vec(idx_r1); + A1 = A_vec(idx_r1,:); + coda1_row = codaM(:,:,idx_r1); + for idx_r2 = 1:idx_r1 + r2 = R(idx_r2); + t0_2 = t0_vec(idx_r2); % delta_T is the overlapping interval between E(r1,t) and E(r2,T-r) - delta_T = T-t0_R(idx_r1) - t0_R(idx_r2); + delta_T = T - t0_1 - t0_2; % Number of samples involved in the overlapping interval - N_mues = round(delta_T/DT); + N_mues = round(delta_T/DT); - if r1+r2>=(D0-2*DR) && r1+r2<(T+DT)*v && N_mues>=1 + if r1 + r2 >= (D0-2*DR) && r1 + r2 < (T+DT)*v && N_mues >= 1 % Computing (coda1 x delta2) and (delta1 x coda 2) - coda1 = A_R(idx_r1)*coda_R(idx_r2,N_mues+1); - coda2 = A_R(idx_r2)*coda_R(idx_r1,N_mues+1); - coda_delta... - (idx_r1,... - idx_r2) = coda1+coda2; + coda1 = A1.*codaM(N_mues+1,:,idx_r2); + coda2 = A_vec(idx_r2,:) .* coda1_row(N_mues+1,:); + csum = coda1 + coda2; + coda_delta(idx_r1,idx_r2,1:lR) = csum; % Computing the contribution of (coda1 x coda2) % integral coda1(u) coda(T-u) du - x1 = coda_R(idx_r1,1:N_mues+1); - x2 = coda_R(idx_r2,N_mues+1:-1:1); - coda_coda... - (idx_r1,... - idx_r2) = sum(x1.*x2)*DT; + x1 = coda1_row(1:N_mues+1,:); + x2 = codaM(N_mues+1:-1:1,:,idx_r2); + coda_coda(idx_r1,idx_r2,1:lR)= sum(x1.*x2)*DT; % simmetrical calculations - coda_delta... - (idx_r2,... - idx_r1) = coda_delta(idx_r1,idx_r2); - coda_coda... - (idx_r2,... - idx_r1) = coda_coda(idx_r1,idx_r2); + coda_delta(idx_r2,idx_r1,1:lR)= csum; + coda_coda(idx_r2,idx_r1,1:lR)= coda_coda(idx_r1,idx_r2,1:lR); end end end -K_integral = coda_coda+coda_delta; +K_integral = coda_coda+coda_delta; + +for i = 1:lR + K_i = K_integral(:,:,i); + % Computing K(x,y,z) by linear interpolation inside the ellipsoid + K_grid_i = zeros(Nxyz,1); + + if any(cond_interior) + F = griddedInterpolant({R, R}, K_i, 'linear', 'nearest'); + vals = F(r1_grid(cond_interior), r2_grid(cond_interior)); + K_grid_i(cond_interior) = vals; + % replace any remaining NaN + K_grid_i(isnan(K_grid_i)) = max(K_grid_i); + end + K_grid(:,:,i) = K_grid_i; + +end +r_grid1 =... + [r_grid(:,1)+origin(1) r_grid(:,2)+origin(2) r_grid(:,3)+origin(3)]; -% Computing K(x,y,z) by linear interpolation inside the ellipsoid -c2 = cond_interior; -K_grid = zeros(Nx*Ny*Nz,1); -K_grid(c2) =... - interp2(R1,R2,K_integral,r1_grid(c2),r2_grid(c2)); -K_grid(isnan(K_grid)) = max(K_grid); -r_grid1 =... - [r_grid(:,1)+origin(1) r_grid(:,2)+origin(2) r_grid(:,3)+origin(3)]; diff --git a/bin/Murat_location.m b/bin/Murat_location.m index 384f11b..364eecf 100644 --- a/bin/Murat_location.m +++ b/bin/Murat_location.m @@ -1,5 +1,4 @@ -function [locationsDeg_i,locationsM_i]... - = Murat_location(origin,SAChdr_i) +function [locationsDeg_i,locationsM_i] = Murat_location(origin,SAChdr_i) % function [locationsDeg_i, locationsM_i] = Murat_location(origin,SAChdr_i) % % CREATES the variable with event and station coordinates @@ -15,10 +14,10 @@ % locationsM_i: locations in meters -even = [SAChdr_i.event.evla SAChdr_i.event.evlo... +even = [SAChdr_i.event.evla SAChdr_i.event.evlo... SAChdr_i.event.evdp*1000]; -stati = [SAChdr_i.station.stla SAChdr_i.station.stlo... +stati = [SAChdr_i.station.stla SAChdr_i.station.stlo... SAChdr_i.station.stel]; if find(even == -12345) diff --git a/bin/Murat_lsqlinQmean.m b/bin/Murat_lsqlinQmean.m index 931b9a2..3bdd073 100644 --- a/bin/Murat_lsqlinQmean.m +++ b/bin/Murat_lsqlinQmean.m @@ -1,7 +1,7 @@ -function [d1, const_Qc_k, constQmean_k, equationQ] =... - Murat_lsqlinQmean(tCm,tWm,Q_k,cf_k,sped,luntot_k,time0_k,rapsp_k) -% function [d1, const_Qc_k, constQmean_k, equationQ] =... -% Murat_lsqlinQmean(tCm,tWm,Q_k,cf_k,sped,luntot_k,time0_k,rapsp_k) +function [d0, Q0, c] = Murat_lsqlinQmean(tCm,tWm,Q_k,cf_k,D,l,... + time0_k,rapsp_k) +% [d0, Q0, c] = Murat_lsqlinQmean(tCm,tWm,Q_k,cf_k,D,l,... +% time0_k,rapsp_k) % % INVERTS with minimum least squares to obtain average Q % @@ -10,8 +10,8 @@ % tWm: coda window length % Q_k: average coda attenuation % cf_k: central frequency -% sped: spectral decay -% luntot_k: total length per frequency +% D: diffusion constant +% l: total ray length in km % time0_k: travel time per frequency % rapsp_k: energy ratio per frequency % @@ -22,33 +22,23 @@ % equationQ: equation to be compared with data in test %% -% Data creation for the true inversion, removing the pre-calculated -% parameters. +% Data creation for the true inversion, removing the parameters +% pre-calculated using the diffusion model -% Include info on Qc -const_Qc_k = (tCm+tWm/2).^-sped... - .*exp(-2*pi*Q_k*cf_k.*(tCm+tWm/2)); -% Data vector for inversion of average Q -d0 =... - log(rapsp_k)/2/pi/cf_k + log(const_Qc_k)/2/pi/cf_k; +tLapse = tCm+tWm/2; +c = (1.5*log(4*pi*D*tLapse)... + + l.^2/4/D./tLapse... + + 2*pi*Q_k*cf_k.*tLapse)... + /2/pi/cf_k; -G = -log(luntot_k)/2/pi/cf_k; -G(:,2) = -time0_k; +data = log(rapsp_k.*l.^2)/2/pi/cf_k; -cova = (G'*G)^(-1)*G'*cov(d0)*G*(G'*G)^(-1); +d0 = data-c; -% Storing inverted parameters -constQmean_k(:,1) = lsqlin(G,d0(:,1)); -constQmean_k(:,2) = sqrt(diag(cova)); - -% Calculate data vector for average Q and geometrical spreading -d1 = d0 + ... - constQmean_k(1,1)*log(luntot_k)/2/pi/cf_k... - + time0_k*constQmean_k(2,1); +G = -time0_k; -% Equation for average Q to fit data -equationQ = -log(const_Qc_k)... - -constQmean_k(1,1)*log(luntot_k)... - -2*pi*cf_k*time0_k*constQmean_k(2,1); +% Storing inverted parameters +Q0 = lsqlin(G,d0); +Q0(2) = (G'*G)^(-1)*G'*cov(d0)^-1*G*(G'*G)^(-1); end \ No newline at end of file diff --git a/bin/Murat_minimiseCGLS.m b/bin/Murat_minimiseCGLS.m deleted file mode 100644 index 5b67dc3..0000000 --- a/bin/Murat_minimiseCGLS.m +++ /dev/null @@ -1,140 +0,0 @@ -function [image, minimizeVector,infoVector,minimizeValue] =... - Murat_minimiseCGLS(outputLCurve,G,bQ,lCurveQc_k,name) -% function [image, minimizeVector,infoVector,minimizeValue] =... -% Murat_minimise(outputLCurve,G,bQ,lCurveQc_k,name) -% MINIMIZES both L2 and I cost functions with conjugate gradients -% CALCULATES the damping value and PLOTS the costs. -% -% Input parameters: -% outputLCurve: show L-curve or not -% G: inversion matrix -% bQ: data vector -% lCurveQc_k: preset damping values -% name: name of the figure -% -% Output parameters: -% image: L-curves and cost functions -% minimizeVector: best solution minimizing both costs -% infoVector: informations related to minimization -% minimizeValue: regularization parameter - -M = size(G,2); -epsilon = zeros(65,1); - -for j=1:59 - if j<=6 - epsilon(j,1) = 10^-9*10^j; - elseif j>6 && j<=15 - epsilon(j,1) = 10^-3 + 10^-3*(j-6); - elseif j>15 && j<=24 - epsilon(j,1) = 10^-2 + 10^-2*(j-15); - elseif j>24 && j<=33 - epsilon(j,1) = 10^-1 + 10^-1*(j-24); - elseif j>33 && j<=42 - epsilon(j,1) = 1 + j - 33; - elseif j>42 && j<=51 - epsilon(j,1) = 10 + (j-42)*10; - elseif j>51 && j<=60 - epsilon(j,1) = 100 + (j-51)*100; - end - -end -epsilon(60:65) = 10.^(3:8); -Nit = length(epsilon); - -epsilonRND = zeros(Nit,7); -mestI = zeros(M,Nit); -mestL2 = zeros(M,Nit); - -epsilonRND(:,1) = epsilon; -for i=1:Nit - epsilon_i = epsilon(i,1); - - optionsI =... - IRset('MaxIter', 500,'RegMatrix','Identity','IterBar','off',... - 'verbosity','off','RegParam', epsilon_i); - - [mestI_i,infoI] = IRcgls(G,bQ,optionsI); - epsilonRND(i,2) = infoI.StopReg.Rnrm; - epsilonRND(i,3) = infoI.StopReg.Xnrm; - epsilonRND(i,4) = infoI.StopReg.NE_Rnrm; - - optionsL2 =... - IRset('MaxIter', 500,'RegMatrix','Laplacian1D','IterBar','off',... - 'verbosity','off','RegParam', epsilon_i); - - [mestL2_i,infoL2] = IRcgls(G,bQ,optionsL2); - - epsilonRND(i,5) = infoL2.StopReg.Rnrm; - epsilonRND(i,6) = infoL2.StopReg.Xnrm; - epsilonRND(i,7) = infoL2.StopReg.NE_Rnrm; - - mestI(:,i) = mestI_i; - mestL2(:,i) = mestL2_i; -end - -costFunctionI = 0.5*sum(epsilonRND(:,2:4).^2,2); -[minCostI,indexCostI] = min(costFunctionI); - -disp(['Minimum of I cost function is ',num2str(minCostI)... - ' for epsilonI = ' num2str(epsilon(indexCostI))]); - -costFunctionL2 = 0.5*sum(epsilonRND(:,5:7).^2,2); - -[minCostL2,indexCostL2] = min(costFunctionL2); - -disp(['Minimum of L2 cost function is ',num2str(minCostL2)... - ' for epsilonL2 = ' num2str(epsilon(indexCostL2))]); - -image = figure('Name',name,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); - -subplot(2,2,1) -loglog(epsilonRND(:,2),epsilonRND(:,3),'k','LineWidth',2); -title('I regularization') -xlabel('Model perturbation'); -ylabel('Data fit'); -for k = 1:2:Nit - text(epsilonRND(k,2),epsilonRND(k,3),num2str(epsilon(k))); -end -SetFDefaults() - -subplot(2,2,2) -loglog(epsilonRND(:,5),epsilonRND(:,6),'k','LineWidth',2); -title('L2 regularization') -xlabel('Model perturbation'); -ylabel('Data fit'); -for k = 1:2:Nit - text(epsilonRND(k,5),epsilonRND(k,6),num2str(epsilon(k))); -end -SetFDefaults() - -subplot(2,2,3) -loglog(epsilon,costFunctionI,'k','LineWidth',2); -title('I Cost') -xlabel('Regularization Parameter'); -ylabel('Data fit'); -SetFDefaults() - -subplot(2,2,4) -loglog(epsilon,costFunctionL2,'k','LineWidth',2); -title('L2 Cost') -xlabel('Regularization Parameter'); -ylabel('Data fit'); -SetFDefaults() - -if outputLCurve == 1 - minimizeValue =... - input('Your damping parameter: '); -else - minimizeValue = lCurveQc_k; -end - - -options =... - IRset('MaxIter', 500,'RegMatrix','Identity','IterBar','off',... - 'verbosity','off','RegParam', minimizeValue); - -[minimizeVector,infoVector] = IRcgls(G,bQ,options); - -end \ No newline at end of file diff --git a/bin/Murat_minimiseHybrid.m b/bin/Murat_minimiseHybrid.m deleted file mode 100644 index 0534774..0000000 --- a/bin/Murat_minimiseHybrid.m +++ /dev/null @@ -1,51 +0,0 @@ -function [image, minimizeVector,infoVector,minimizeValue] =... - Murat_minimiseHybrid(outputLCurve,G,bQ,lCurveQc_k,name) -% function [image, minimizeVector,infoVector,minimizeValue] =... -% Murat_minimiseIR(G,bQ,name) -% MINIMIZES weighted least square solution with an hybrid iterative method -% based on generalised cross validation -% CALCULATES the damping value and PLOTS the costs. -% -% Input parameters: -% G: inversion matrix -% bQ: data vector -% name: name of the figure -% -% Output parameters: -% image: L-curves and cost functions -% minimizeVector: best solution minimizing both costs -% infoVector: informations related to minimization -% minimizeValue: regularization parameter - -options = IRset('RegParam', 'wgcv', 'NoStop', 'on',... - 'IterBar','off','verbosity','off'); -[~, infoVector] = IRhybrid_lsqr(G, bQ, options); -Reg = infoVector.StopReg; -It = Reg.It; -minimizeVector = Reg.X; -minimizeValue = Reg.RegP; -Res = infoVector.Rnrm; - -image = figure('Name',name,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); - -plot(Res,'k','LineWidth',2); -hold on -scatter(It,Res(It),100,'filled','r'); -text(It+10,Res(It),['Reg. Parameter = ' num2str(minimizeValue)],... - 'FontSize',15); -hold off -title('Regularization') -xlabel('Iteration'); -ylabel('Cost Function'); -SetFDefaults() - -if outputLCurve == 1 - minimizeValue = lCurveQc_k; - options = IRset('RegParam', lCurveQc_k, 'NoStop', 'on',... - 'IterBar','off','verbosity','off'); - [minimizeVector, infoVector] = IRhybrid_lsqr(G, bQ, options); -end - - -end \ No newline at end of file diff --git a/bin/Murat_modv1D.m b/bin/Murat_modv1D.m index 2d76cb5..e85b844 100644 --- a/bin/Murat_modv1D.m +++ b/bin/Murat_modv1D.m @@ -1,5 +1,4 @@ -function [modv,pvel,modvPlot]... - =... +function [modv,pvel,modvPlot] =... Murat_modv1D(modvXYZ,modvOriginal,PorS,origin) % function [modv,pvel,modvPlot]=... % Murat_modv1D(modvXYZ,modvOriginal,PorS,origin) @@ -16,30 +15,30 @@ % pvel: propagation velocity model in matrix % modvPlot: velocity model to be plot -modv = modvXYZ; -z1D = -modvOriginal(:,1)*1000; -v1D = modvOriginal(:,PorS+1); +modv = modvXYZ; +z1D = -modvOriginal(:,1)*1000; +v1D = modvOriginal(:,PorS+1); for k=1:length(modv(:,1)) - zModv_k = modv(k,3); - [~,indexModv] = min(abs(z1D-zModv_k)); - modv(k,4) = v1D(indexModv); + zModv_k = modv(k,3); + [~,indexModv] = min(abs(z1D-zModv_k)); + modv(k,4) = v1D(indexModv); end % Nodes of the original velocity model -xD = unique(modv(:,1)); -yD = unique(modv(:,2)); -zD = sort(unique(modv(:,3)),'descend'); +xD = unique(modv(:,1)); +yD = unique(modv(:,2)); +zD = sort(unique(modv(:,3)),'descend'); % Create its meshgrid -[~,~,~,pvel] = Murat_fold(xD,yD,zD,modv(:,4)); +[~,~,~,pvel] = Murat_fold(xD,yD,zD,modv(:,4)); % Change to lat/lon for plotting -wgs84 = wgs84Ellipsoid("m"); -d = sqrt(modv(:,1).^2 + modv(:,2).^2); -az = atan(modv(:,1)./modv(:,2))*360/2/pi; -az(isnan(az)) = 0; -[lat2,lon2] = reckon(origin(1),origin(2),d,az,wgs84); -modvPlot = [lon2 lat2 modv(:,3:4)]; +wgs84 = wgs84Ellipsoid("m"); +d = sqrt(modv(:,1).^2 + modv(:,2).^2); +az = atan(modv(:,1)./modv(:,2))*360/2/pi; +az(isnan(az)) = 0; +[lat2,lon2] = reckon(origin(1),origin(2),d,az,wgs84); +modvPlot = [lon2 lat2 modv(:,3:4)]; end diff --git a/bin/Murat_modv3D.m b/bin/Murat_modv3D.m index c5ad8b0..1818612 100644 --- a/bin/Murat_modv3D.m +++ b/bin/Murat_modv3D.m @@ -1,5 +1,4 @@ -function [modvProp,modvP,modvEqSpace,modvPlot]... - =... +function [modvProp,modvP,modvEqSpace,modvPlot] =... Murat_modv3D(FPath,FLabel,modvOriginal,origin,mLat,mLon,nLat,nLon,nzc) % function [modvP,modvPropagation,modvEqS,modvPlot] =... % Murat_modv3D(FPath,FLabel,modvOriginal,origin,mLat,mLon,nLat,nLon) @@ -23,46 +22,41 @@ %% Rescale original lat/lon modV in cartesian coords % Transform nodes velocity model in meters with WGS84 -wgs84 = wgs84Ellipsoid("m"); -[d_o,az_o] = distance(origin(1),origin(2),... +wgs84 = wgs84Ellipsoid("m"); +[d_o,az_o] = distance(origin(1),origin(2),... modvOriginal(:,1),modvOriginal(:,2),wgs84); % Transform min/max lat/lon earthquakes in meters with WGS84 -[d_ll,az_ll] = distance(origin(1),origin(2),... - mLat',mLon',wgs84); -mLatLon = [d_ll.*sin(az_ll*2*pi/360)... - d_ll.*cos(az_ll*2*pi/360)]; +[d_ll,az_ll] = distance(origin(1),origin(2),mLat',mLon',wgs84); +mLatLon = [d_ll.*sin(az_ll*2*pi/360) d_ll.*cos(az_ll*2*pi/360)]; %% Original velocity model in meters - uneven spacing -modvOrC = sortrows([d_o.*sin(az_o*2*pi/360)... +modvOrC = sortrows([d_o.*sin(az_o*2*pi/360)... d_o.*cos(az_o*2*pi/360) modvOriginal(:,3:4)],[1,2,-3]); -xOrC = modvOrC(:,1); -yOrC = modvOrC(:,2); -zOrC = modvOrC(:,3); -vOrC = modvOrC(:,4); +xOrC = modvOrC(:,1); +yOrC = modvOrC(:,2); +zOrC = modvOrC(:,3); +vOrC = modvOrC(:,4); %% Velocity model for ray tracing - inversion % For ray-tracing, you need to space evenly with the same number of % x-y nodes as in input - here we extend to min and max quake locations -lModvP = [min(mLatLon(1,1),min(xOrC))... +lModvP = [min(mLatLon(1,1),min(xOrC))... max(mLatLon(2,1),max(xOrC)); min(mLatLon(1,2),min(yOrC))... max(mLatLon(2,2),max(yOrC))]; -xM1 = linspace(lModvP(1,1),lModvP(1,2),nLon)'; -yM1 = linspace(lModvP(2,1),lModvP(2,2),nLat)'; -zM1 =... - sort(linspace(min(zOrC),max(zOrC),nzc),'descend'); +xM1 = linspace(lModvP(1,1),lModvP(1,2),nLon)'; +yM1 = linspace(lModvP(2,1),lModvP(2,2),nLat)'; +zM1 = sort(linspace(min(zOrC),max(zOrC),nzc),'descend'); [XEqS,YEqS,ZEqS,modvEqSpace,modvEqS] =... Murat_rescale(xOrC,yOrC,zOrC,vOrC,xM1,yM1,zM1); % Change to lat/lon for plotting -d =... - sqrt(modvEqSpace(:,1).^2 + modvEqSpace(:,2).^2); -az =... - atan(modvEqSpace(:,1)./modvEqSpace(:,2))*360/2/pi; +d = sqrt(modvEqSpace(:,1).^2 + modvEqSpace(:,2).^2); +az = atan(modvEqSpace(:,1)./modvEqSpace(:,2))*360/2/pi; % Condition to avoid NaN if ~isempty(isnan(az)) @@ -71,20 +65,19 @@ end -[lat2,lon2] = reckon(origin(1),origin(2),d,az,wgs84); -modvPlot = [lon2 lat2 modvEqSpace(:,3:4)]; +[lat2,lon2] = reckon(origin(1),origin(2),d,az,wgs84); +modvPlot = [lon2 lat2 modvEqSpace(:,3:4)]; %Interpolated model for ray-tracing - half grid step of original -resol2x = abs(xM1(2)-xM1(1))/2; -resol2y = abs(yM1(2)-yM1(1))/2; -resol2z = abs(zM1(2)-zM1(1))/2; +resol2x = abs(xM1(2)-xM1(1))/2; +resol2y = abs(yM1(2)-yM1(1))/2; +resol2z = abs(zM1(2)-zM1(1))/2; -xp = xM1(1):resol2x:xM1(end); -yp = yM1(1):resol2y:yM1(end); -zp = zM1(1):-resol2z:zM1(end); +xp = xM1(1):resol2x:xM1(end); +yp = yM1(1):resol2y:yM1(end); +zp = zM1(1):-resol2z:zM1(end); -[Xq,Yq,Zq,modvProp,modvP] =... - Murat_rescale(xOrC,yOrC,zOrC,vOrC,xp,yp,zp); +[Xq,Yq,Zq,modvProp,modvP]= Murat_rescale(xOrC,yOrC,zOrC,vOrC,xp,yp,zp); Murat_test_vel_models(FPath,FLabel,modvOriginal,modvOrC,... XEqS,YEqS,ZEqS,modvEqS,Xq,Yq,Zq,modvP); diff --git a/bin/Murat_originTime.m b/bin/Murat_originTime.m index bba860c..29ad0c0 100644 --- a/bin/Murat_originTime.m +++ b/bin/Murat_originTime.m @@ -18,23 +18,22 @@ % originTime_i: output origin time if isequal(originTime,[]) || isequal(eval(originTime),-12345) - - Distance =... - sqrt((sst_i(4)-sst_i(1))^2+(sst_i(5)-sst_i(2))^2+... - (sst_i(6)-sst_i(3))^2); - theoreticalTime_i = Distance/v/1000; - originTime_i = pktime_i-theoreticalTime_i; - + Distance = sqrt((sst_i(4)-sst_i(1))^2 +... + (sst_i(5)-sst_i(2))^2 + (sst_i(6)-sst_i(3))^2); + + theoreticalTime_i = Distance/v/1000; + originTime_i = pktime_i-theoreticalTime_i; + else - - originTime_i = eval(originTime); - theoreticalTime_i = pktime_i - originTime_i; + + originTime_i = eval(originTime); + theoreticalTime_i = pktime_i - originTime_i; if theoreticalTime_i < 0 - error(['The picking is set before the origin time for recording '... - num2str(i_label)]); + flag = num2str(i_label); + error(['Picking set before the origin time, recording ' flag]); end - + end end diff --git a/bin/Murat_paasschensFunction.m b/bin/Murat_paasschensFunction.m index df17431..7394be1 100644 --- a/bin/Murat_paasschensFunction.m +++ b/bin/Murat_paasschensFunction.m @@ -26,7 +26,7 @@ % r: distance to source % v: velocity % B0: albedo -% Le_1: extinction length +% Le_1: inverse extinction length % dt: time resolution % T: final time of interest % @@ -40,28 +40,36 @@ % Authors: De La Torre & Del Pezzo, used first in Del Pezzo et al. 2018, % Geosciences. -t0 = r/v; -t = (t0:dt:(T+2*dt))'; -N = length(t); +t0 = r/v; +t = (t0:dt:(T+2*dt))'; +N = numel(t); -A0 = exp(-Le_1*v*t0)./(4*pi*r.^2*v); +A0 = exp(-Le_1*v*t0)./(4*pi*r.^2*v); -f1 = (1-t0.*t0./(t.*t)).^(1/8); -f2 = (3*B0*Le_1./(4*pi*v*t)).^(3/2); -f3 = exp(-Le_1*v*t); -x = v*t*B0*Le_1.*(1-t0.*t0./(t.*t)).^(3/4); -f4 = F_function(x); -coda = f1.*f2.*f3.*f4; -coda(1) = coda(2)*2/3; +% Precompute recurring terms +t_inv = 1 ./ t; +t2 = t .* t; +t0_over_t = t0 .* t0 ./ t2; +one_minus = 1 - t0_over_t; +one_minus(one_minus < 0) = 0; -return; -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +f1 = one_minus .^ (1/8); +const_f2 = (3 * B0 .* Le_1) / (4 * pi * v); -function F = F_function(x) -cond = x>1e-30; -F = zeros(size(x)); -y = x(cond); -F(cond) = exp(y).*sqrt(1+2.026./(y+1e-30)); +f2 = (const_f2 .* t_inv) .^ (3/2); -return; -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file +f3 = exp(-Le_1*v.*t); + +f1_pow = one_minus .^ (3/4); +x = v*t.*B0.*Le_1.*f1_pow; + +% Inline F_function: F = exp(x) .* sqrt(1 + 2.026 ./ x) for x > tiny +F = zeros(size(x)); +cond = x > 1e-30; +y = x(cond); +F(cond) = exp(y) .* sqrt(1 + 2.026 ./ y); + +coda = f1.*f2.*f3.*F; +coda(1) = coda(2)*2/3; + +end \ No newline at end of file diff --git a/bin/Murat_peakDelay.m b/bin/Murat_peakDelay.m index 00c49ae..5e16ea2 100644 --- a/bin/Murat_peakDelay.m +++ b/bin/Murat_peakDelay.m @@ -1,4 +1,4 @@ -function peakDelay_i =... +function peakDelay_i =... Murat_peakDelay(sp_i,cursorPick_i,srate_i,cursorPeakDelay_i) % function peakDelay_i =... % Murat_peakDelay(sp_i,cursorPick_i,srate_i,cursorPeakDelay_i) @@ -14,16 +14,14 @@ % Output parameters: % peakDelay_i: peak delay on trace -lsp = size(sp_i,2); -peakDelay_i = zeros(lsp,1); +% Extract the windowed matrix: rows from pick to peakDelay, all columns +window = sp_i(cursorPick_i:cursorPeakDelay_i, :); -for i = 1:lsp - pdsp = sp_i(cursorPick_i:cursorPeakDelay_i,i); +% Find index of max within each column (relative to window start) +[~, idx] = max(window, [], 1); - [~,timeMaxAmplitude] = max(pdsp); - - peakDelay_i(i) = timeMaxAmplitude/srate_i; -end +% Convert to seconds and return as column vector +peakDelay_i = (idx.' ) / srate_i; end diff --git a/bin/Murat_peakDelayCheck.m b/bin/Murat_peakDelayCheck.m index 45c376e..2b3edd9 100644 --- a/bin/Murat_peakDelayCheck.m +++ b/bin/Murat_peakDelayCheck.m @@ -1,4 +1,4 @@ -function cursorPeakDelay_i =... +function cursorPeakDelay_i =... Murat_peakDelayCheck(tempis,cursorPick_i,maxtpde,srate_i) % function cursorPeakDelay_i =... % Murat_peakDelayCheck(tempis,cursorPick_i,maxtpde,srate_i) @@ -14,22 +14,14 @@ % Output parameters: % cursorPeakDelay_i: location of peak delay window -cursorPeakDelayMax = floor(cursorPick_i+maxtpde*srate_i); -lengthTempis = length(tempis); +cursorPeakDelayMax = floor(cursorPick_i+maxtpde*srate_i); +lengthTempis = numel(tempis); %Measures for peak delay - compute shorter window if waveform is cut -if cursorPeakDelayMax > lengthTempis - - cursorPeakDelay_i = lengthTempis; - msg =... - 'Length of seismogram shorter than peak-delay window!'; - warning(msg); - -else - - cursorPeakDelay_i = cursorPeakDelayMax; - -end +cursorPeakDelay_i = min(cursorPeakDelayMax, lengthTempis); +if cursorPeakDelay_i < cursorPeakDelayMax + warning('Length of seismogram shorter than peak-delay window!'); +end -end +end \ No newline at end of file diff --git a/bin/Murat_picking.m b/bin/Murat_picking.m index 4634e88..ee6f498 100644 --- a/bin/Murat_picking.m +++ b/bin/Murat_picking.m @@ -1,5 +1,5 @@ -function [cursorPick_i, pktime_i, v_i] =... - Murat_picking(tempis,PTime,STime,PorS,vP,vS,srate_i,listaSAC_i,SAChdr) %#ok +function [cursorPick_i, pktime_i, v_i] = Murat_picking(tempis,PTime,... + STime,PorS,vP,vS,srate_i,listaSAC_i,SAChdr) %#ok % CHECKS if you are working with P or S picking and if this picking is % inside the waveform. % @@ -11,7 +11,6 @@ % vP: P-wave velocity % vS: S-wave velocity model % srate_i: sampling rate -% SAChdr: SAC header from trace rate % % Output parameters: % cursorPick_i: position of the picking on the trace @@ -19,26 +18,19 @@ % v_i: chosen average velocity if PorS == 2 - pktime_i = eval(PTime); - v_i = vP; + pktime_i = eval(PTime); + v_i = vP; elseif PorS == 3 - pktime_i = eval(STime); - v_i = vS; + pktime_i = eval(STime); + v_i = vS; end % If picking is not on the waveform if pktime_i < tempis(1) - error(['The picking is set before the start of the waveform '... - listaSAC_i]) + error(['Picking is before the start of the waveform ' listaSAC_i]) elseif pktime_i > tempis(end) - error(['The picking is set after the end of the waveform '... - listaSAC_i]) + error(['Picking is after the end of the waveform ' listaSAC_i]) end -t00 = tempis(1); - -cursorPick_i = floor((pktime_i-t00)*srate_i); -end - - - +cursorPick_i = floor((pktime_i-tempis(1))*srate_i); +end \ No newline at end of file diff --git a/bin/Murat_plot.m b/bin/Murat_plot.m index a3321e0..b7d3df4 100644 --- a/bin/Murat_plot.m +++ b/bin/Murat_plot.m @@ -1,146 +1,114 @@ %% MURAT_PLOT Creates files for visualization in Matlab -function Murat = Murat_plot(Murat) -%% +function Murat = Murat_plot(Murat) + +% Refactored input unpacking and preallocation +s = Murat.input; + % Importing all the necessary inputs and data for plotting -FLabel = Murat.input.label; -origin = Murat.input.origin; -ending = Murat.input.end; -x = Murat.input.x; -y = Murat.input.y; -z = Murat.input.z; -sections = Murat.input.sections; -cf = Murat.input.centralFrequency; -vS = Murat.input.averageVelocityS; -tWm = Murat.input.codaWindow; -kT = Murat.input.tresholdNoise; -B0 = Murat.input.albedo; -Le1 = Murat.input.extinctionLength; -QcM = Murat.input.QcMeasurement; -sped = Murat.input.spectralDecay; -lapseTimeMethod = Murat.input.lapseTimeMethod; - -modvQc = Murat.input.modv; -stepgX = (modvQc(2,1) - modvQc(1,1))/2; -stepgY = (modvQc(2,2) - modvQc(1,2))/2; -stepgZ = (modvQc(2,3) - modvQc(1,3))/2; -modvQc(:,1) = modvQc(:,1) + stepgX; -modvQc(:,2) = modvQc(:,2) + stepgY; -modvQc(:,3) = modvQc(:,3) + stepgZ; - -Qm = Murat.Qc.inverseQc; -time0 = Murat.rays.travelTime; -retainPeakDelay = Murat.PD.retainPeakDelay; -retainQc = Murat.Qc.retainQc; -retainQ = Murat.Q.retainQ; -ray_crosses_pd = Murat.PD.raysPeakDelay; -ray_crosses_Qc = Murat.Qc.raysQc; -ray_crosses_Q = Murat.Q.raysQ; -fitrobust = Murat.PD.fitrobust; -peakData = Murat.PD.peakDelay; -luntot = Murat.rays.totalLengthRay; -rma = Murat.rays.raysPlot; -modv_pd = Murat.PD.modvPeakDelay; -modv_Qc = Murat.Qc.modvQc; -modv_Q = Murat.Q.modvQ; -evestazDegrees = Murat.rays.locationsDeg; -energyRatio = Murat.Q.energyRatioBodyCoda; -codaNoiseRatio = Murat.Q.energyRatioCodaNoise; -Ac_i = Murat.Qc.inversionMatrixQc; -RZZ = Murat.Qc.uncertaintyQc; -A_i = Murat.Q.inversionMatrixQ; -residualQc = Murat.Qc.residualQc; -residualQ = Murat.Q.residualQ; -locationM = Murat.rays.locationsM; -tCoda = Murat.Qc.tCoda; - -FPath = './'; -sizeTitle = 18; - -lMF = size(ray_crosses_pd); -sections(3) = sections(3)/1000; +FLabel = s.label; +origin = s.origin; +ending = s.end; +x = s.x; +y = s.y; +z = s.z; +sections = s.sections; +cf = s.centralFrequency; +tWm = s.codaWindow; +D = s.DiffConstant; +PlotRays = s.PlotRays; +PlotTests = s.PlotTests; +PlotResults = s.PlotResults; +PlotCheckers = s.PlotCheckers; +PlotSpikes = s.PlotSpikes; +PlotParameters = s.PlotParameters; +CoordGrid = s.DDcoordinates; + +luntot = Murat.rays.totalLengthRay; +rma = Murat.rays.raysPlot; +time0 = Murat.rays.travelTime; +evstDegrees = Murat.rays.locationsDeg; +retainPeakDelay = Murat.PD.retainPeakDelay; +rayChPD = Murat.PD.raysPeakDelay; +fitrobust = Murat.PD.fitrobust; +peakData = Murat.PD.peakDelay; +modv_pd = Murat.PD.modvPeakDelay; +Qm = Murat.Qc.inverseQc; +retainQc = Murat.Qc.retainQc; +Ac_i = Murat.Qc.inversionMatrixQc; +RZZ = Murat.Qc.uncertaintyQc; +residualQc = Murat.Qc.residualRedQc; +tCoda = Murat.Qc.tCoda; +ray_crosses_Qc = Murat.Qc.raysQc; +modv_Qc = Murat.Qc.modvQc; +retainQ = Murat.Q.retainQ; +modv_Q = Murat.Q.modvQ; +energyRatio = Murat.Q.energyRatioBodyCoda; +codaNoiseRatio = Murat.Q.energyRatioCodaNoise; + +sTitle = 18; + +lMF = size(rayChPD); +sections(3) = sections(3)/1000; %% PLOTS - coverage and sensitivity -% Declustering is done before any frequency analysis, here we show the 2D -% rays before and after -if Murat.input.declustering > 0 - locDegOriginal = Murat.rays.locDegOriginal; - FName_Cluster = 'Clustering'; - clustering = Murat_imageDeclustering(... - locDegOriginal,evestazDegrees,origin,ending,FName_Cluster); - storeFolder = 'Tests'; - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_Cluster); - saveas(clustering,pathFolder,'png'); - close(clustering) -end +evst = [evstDegrees(:,1:2) -evstDegrees(:,3)/1000 ... + evstDegrees(:,4:5) evstDegrees(:,6)/1000]; -evestaz =... - [evestazDegrees(:,1:2) -evestazDegrees(:,3)/1000 ... - evestazDegrees(:,4:5) evestazDegrees(:,6)/1000]; +avQcFreq = zeros(2,lMF(2)); -averageQcFrequency = zeros(2,lMF(2)); +% small helper to build and save path once +makePath = @(varargin) fullfile('./', FLabel, varargin{:}); for k = 1:lMF(2) + %% + D_k = D(k); + cf_k = cf(k); + fcName = strrep(num2str(cf_k),'.','_'); + rtpdk = retainPeakDelay(:,k); + rtQk = retainQ(:,k); + rtQck = retainQc(:,k); + rcQck = ray_crosses_Qc(:,k); + modv_pd_k = modv_pd(:,:,k); + modv_Qc_k = modv_Qc(:,:,k); + modv_Q_k = modv_Q(:,:,k); + [X,Y,Z1,mPD]= Murat_fold(x,y,z,modv_pd_k(:,4)); + [~,~,~,mQc] = Murat_fold(x,y,z,modv_Qc_k(:,4)); + [~,~,~,mQ] = Murat_fold(x,y,z,modv_Q_k(:,4)); + Z = Z1/1000; + evst_Qc = evst(rtQck,:); + evst_pd = evst(rtpdk,:); + evst_Q = evst(rtQk,:); + Qm_k = Qm(rtQck,k); + RZZ_k = RZZ(rtQck,k); + avQcFreq(1,k) = sum(RZZ_k.*Qm_k)/sum(RZZ_k); + avQcFreq(2,k) = std(Qm_k); + + if PlotRays == 1 % Murat_plot starts plotting the ray distribution if asked by the user. % It stores the files in the corresponding folder. - storeFolder = 'Rays'; - cf_k = cf(k); - fcName = num2str(cf_k); - if find(fcName == '.') - fcName(fcName == '.') = '_'; - end - rtpdk = retainPeakDelay(:,k); - rtQk = retainQ(:,k); - rcQk = ray_crosses_Q(:,k); - rtQck = retainQc(:,k); - rcQck = ray_crosses_Qc(:,k); + storeFolder = 'Rays'; - %% - % The rays are visualized for different techniques, starting with the peak delay - FName_peakDelay = ['Rays_PeakDelay_' fcName '_Hz']; - rma_pd = rma(:,2:4,rtpdk)/1000; - evestaz_pd = evestaz(rtpdk,:); - rays_peakDelay = Murat_imageRays(rma_pd,origin,... - ending,evestaz_pd,x,y,z,FName_peakDelay); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_peakDelay); - saveas(rays_peakDelay,pathFolder,'png'); + % Peak Delay rays + FName_peakDelay = ['Rays_PeakDelay_' fcName '_Hz']; + rma_pd = rma(:,2:4,rtpdk)/1000; + + rays_peakDelay = Murat_imageRays(rma_pd,origin,ending,evst_pd,... + x,y,z,FName_peakDelay); + saveFigureAsImage(makePath(storeFolder, FName_peakDelay)); close(rays_peakDelay) %% % The next figure shows the rays for the total attenuation (Q) - FName_Q = ['Rays_Q_' fcName '_Hz']; - rma_Q = rma(:,2:4,rtQk)/1000; - evestaz_Q = evestaz(rtQk,:); - rays_Q =... - Murat_imageRays(rma_Q,origin,ending,evestaz_Q,x,y,z,FName_Q); - pathFolder =... - fullfile(FPath, FLabel, storeFolder, FName_Q); - saveas(rays_Q,pathFolder,'png'); - close(rays_Q) - - %% - % The next figure checks the sensitivity of coda attenuation - % measurements. The code creates figures that show sections in the - % sensitivity kernels. The left panel shows the sensitivity kernel in - % the full space while the rigth panel shows the normalized - % kernel in the inversion grid. - storeFolder = 'Kernels'; - FName_Qc = ['Kernel_Qc' fcName '_Hz']; - kernels = figure('Name',FName_Qc,... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); - - % Calculates kernels - [K_grid, r_grid] =... - Murat_kernels(tCoda(1)+tWm/2,locationM(1,1:3),locationM(1,4:6),... - modvQc,vS,kT,B0,Le1,lapseTimeMethod); - - Murat_codaMatrix(modvQc,K_grid,r_grid,1,origin,sections); - pathFolder =... - fullfile(FPath, FLabel, storeFolder, FName_Qc); - Murat_saveFigures_2panels(kernels,pathFolder); + FName_Q = ['Rays_Q_' fcName '_Hz']; + rma_Q = rma(:,2:4,rtQk)/1000; - %% Plot - Tests + rays_Q = Murat_imageRays(rma_Q,origin,ending,evst_Q,x,y,z,... + FName_Q); + saveFigureAsImage(makePath(storeFolder, FName_Q)); + close(rays_Q) + + end + % Tests % In this section Murat_plot makes checks on the three parameters. % These plots are always visualised. They check that: % (1) Qc is constant with ray length - also computes weighted average; @@ -148,302 +116,270 @@ % (3) amplitude ratios decay with hypocentral distance. % These plots are used to select measurements and understand how well % they follow the assumptions. - storeFolder = 'Tests/Qc'; - Qm_k = Qm(rtQck,k); - RZZ_k = RZZ(rtQck,k); - residualQc_k = residualQc(k); - luntot_Qc = luntot(rtQck)/1000; - Ac = Ac_i(rtQck,rcQck); - - averageQcFrequency(1,k) = sum(RZZ_k.*Qm_k)/sum(RZZ_k); - averageQcFrequency(2,k) = std(Qm_k); - - Qc_title = ['Qc check ' fcName ' Hz']; - Qc_analysis = Murat_imageCheckQc(Qm_k,RZZ_k,... - residualQc_k,luntot_Qc,Ac,sizeTitle,Qc_title,QcM); - saveas(Qc_analysis, fullfile(FPath,FLabel,storeFolder,... - ['Qc_analysis_' fcName '_Hz']),'png'); - saveas(Qc_analysis, fullfile(FPath,FLabel,storeFolder,... - ['Qc_analysis_' fcName '_Hz'])); - close(Qc_analysis) + + if PlotTests == 1 - %% - % Then it shows the peak delay relative to the travel time. - storeFolder = 'Tests/PeakDelay'; - peakData_k = peakData(rtpdk,k); - fitrobust_k = fitrobust(:,k); - time0PD = time0(rtpdk); - - pd_title = ['Peak Delay check ' fcName ' Hz']; - pd_analysis = Murat_imageCheckPeakDelay(... - time0PD,fitrobust_k,peakData_k,sizeTitle,pd_title); - saveas(pd_analysis, fullfile(FPath,FLabel,storeFolder,... - ['PD_analysis_' fcName '_Hz']),'png'); - saveas(pd_analysis, fullfile(FPath,FLabel,storeFolder,... - ['PD_analysis_' fcName '_Hz'])); - close(pd_analysis) + % Qc test + storeFolder = fullfile('Tests','Qc'); - %% - % Then it plots first the logarithm of the energy ratio versus travel - % time. - storeFolder = 'Tests/Q'; - energyRatio_k = energyRatio(rtQk & rtQck,k); - residualQ_k = residualQ(k); - Edirect_k =... - energyRatio_k./codaNoiseRatio(rtQk & rtQck,k); - A_k = A_i(rtQk & rtQck,rcQk); - Q_k = Qm(rtQk & rtQck,k); - luntot_k = luntot(rtQk & rtQck); - time0_k = time0(rtQk & rtQck); - rapsp_k = energyRatio(rtQk & rtQck,k); - tCm = tCoda(rtQk & rtQck,k); - - CN_title =... - ['Coda Normalization check ' fcName ' Hz']; - [d1, ~,spreadAverageQ, equationQ]... - =... - Murat_lsqlinQmean(tCm,tWm,Q_k,cf_k,sped,luntot_k,time0_k,rapsp_k); - CN_analysis =... - Murat_imageCheckCN(equationQ,residualQ_k,d1,spreadAverageQ,... - luntot_k,time0_k,energyRatio_k,A_k,Edirect_k,CN_title); - saveas(CN_analysis, fullfile(FPath,FLabel,storeFolder,... - ['CN_analysis_' fcName '_Hz']),'png'); - saveas(CN_analysis, fullfile(FPath,FLabel,storeFolder,... - ['CN_analysis_' fcName '_Hz'])); - close(CN_analysis) + residualQc_k= residualQc(k); + luntot_Qc = luntot(rtQck)/1000; + Ac = Ac_i(rtQck,rcQck); + - %% PLOT - RESULTS + + Qc_title = ['Qc check ' fcName ' Hz']; + Qc_analysis = Murat_imageCheckQc(Qm_k,RZZ_k,residualQc_k,... + luntot_Qc,Ac,sTitle,Qc_title); + p = makePath(storeFolder, ['Qc_analysis_' fcName '_Hz']); + saveFigureAsImage(p); + savefig(Qc_analysis, [p '.fig']); + close(Qc_analysis); + + % Peak delay test + storeFolder = fullfile('Tests','PeakDelay'); + peakData_k = peakData(rtpdk,k); + fitrobust_k = fitrobust(:,k); + time0PD = time0(rtpdk); + + pd_title = ['Peak Delay check ' fcName ' Hz']; + pd_analysis = Murat_imageCheckPeakDelay(time0PD,fitrobust_k,... + peakData_k,sTitle,pd_title); + p = makePath(storeFolder, ['PD_analysis_' fcName '_Hz']); + saveFigureAsImage(p); + savefig(pd_analysis, [p '.fig']); + close(pd_analysis); + + % Coda normalization test + storeFolder = fullfile('Tests','Q'); + mask = rtQk & rtQck; + + energyRatio_k = energyRatio(mask,k); + Ed_k = energyRatio_k./codaNoiseRatio(mask,k); + Qc_k = Qm(mask,k); + luntot_k = luntot(mask); + time0_k = time0(mask); + rapsp_k = energyRatio(mask,k); + tCm = tCoda(mask,k); + l = luntot_k/1000; + + CNTitle = ['Coda Normalization check ' fcName ' Hz']; + [d0, Q0, ~] = Murat_lsqlinQmean(tCm,tWm,Qc_k,cf_k,D_k,l,time0_k,... + rapsp_k); + CN_analysis = Murat_imageCheckCN(time0_k,Q0,d0,Ed_k,CNTitle); + p = makePath(storeFolder,['CN_analysis_' fcName '_Hz']); + saveFigureAsImage(p); + savefig(CN_analysis, [p '.fig']); + close(CN_analysis); + + end + + % PLOT - RESULTS + if PlotResults == 1 % Set up matrices. The points are set to the upper SW vertices to % work with the function "slice". All stored in the sub-folder. - modv_pd_k = modv_pd(:,:,k); - modv_Qc_k = modv_Qc(:,:,k); - modv_Q_k = modv_Q(:,:,k); - [X,Y,Z1,mPD] = Murat_fold(x,y,z,modv_pd_k(:,4)); - [~,~,~,mQc] = Murat_fold(x,y,z,modv_Qc_k(:,4)); - [~,~,~,mQ] = Murat_fold(x,y,z,modv_Q_k(:,4)); - Z = Z1/1000; - evestaz_Qc = evestaz(rtQck,:); + storeFolder = fullfile('Results','PeakDelay'); + + - %% % Peak delays results, using interpolation defined by 'divi'. - divi = 5; - storeFolder = 'Results/PeakDelay'; - FName_PDMap = ['Peak-Delay-3D_' fcName '_Hz']; - peakDelaymap = Murat_image3D(X,Y,Z,mPD,... - redblue,sections,evestaz_pd,x,y,z,divi,FName_PDMap); - title('Log. peak-delay variations',... - 'FontSize',sizeTitle,'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath, FLabel, storeFolder, FName_PDMap); - Murat_saveFigures(peakDelaymap,pathFolder); + divi = 5; + FName_PDMap = ['Peak-Delay-3D_' fcName '_Hz']; + peakDelaymap = Murat_image3D(X,Y,Z,mPD,redblue,sections,... + evst_pd,x,y,z,divi,FName_PDMap); + title('Log. peak-delay variations','FontSize',sTitle,... + 'FontWeight','bold','Color','k'); + Murat_saveFigures(peakDelaymap, makePath(storeFolder, FName_PDMap)); - %% % Plots peak delays only keeping cells with more than 'factor'% of data - factor = 5; - - max_positive = max(mPD(mPD >= 0)); - max_negative = min(mPD(mPD < 0)); - positive_threshold = (max_positive / 100) * factor; - negative_threshold = (max_negative / 100) * factor; - keep_bins_positive = mPD > positive_threshold; - keep_bins_negative = mPD < negative_threshold; - keep_bins = keep_bins_positive | keep_bins_negative; + factor = 5; + max_pos = max(mPD(mPD >= 0)); + max_neg = min(mPD(mPD < 0)); + thresholdPlus = (max_pos / 100) * factor; + thresholdMinus = (max_neg / 100) * factor; + keepBinsPositive= mPD > thresholdPlus; + keepBinsNegative= mPD < thresholdMinus; + keep_bins = keepBinsPositive | keepBinsNegative; - mPD_red = mPD.*keep_bins; + mPDRed = mPD.*keep_bins; - FName_PDMap =... + FName_PDMap =... ['Peak-Delay-3D_' fcName '_Hz_',num2str(factor),'_perc']; - [peakDelaymap_red,pd_inter,~,~,~,Xi,Yi,Zi]... - = Murat_image3D(X,Y,Z,mPD_red,... - redblue,sections,evestaz_pd,x,y,z,divi,FName_PDMap); - title('Peak-delay variations','FontSize',sizeTitle,... + [peakDelaymapRed,pd_inter,~,~,~,Xi,Yi,Zi] = Murat_image3D(X,Y,Z,... + mPDRed,redblue,sections,evst_pd,x,y,z,divi,FName_PDMap); + title('Peak-delay variations','FontSize',sTitle,... 'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath, FLabel, storeFolder, FName_PDMap); - Murat_saveFigures(peakDelaymap_red,pathFolder); - + Murat_saveFigures(peakDelaymapRed, makePath(storeFolder, FName_PDMap)); + % interpolated for the parameter map - interp_modv_pd_k = Murat_unfold(Xi,Yi,Zi,pd_inter); + interp_modv_pd_k= Murat_unfold(Xi,Yi,Zi,pd_inter); - %% % Qc results - storeFolder = 'Results/Qc'; - FName_QcMap = ['Qc-3D_' fcName '_Hz']; - [Qcmap,qc_inter,xi,yi,zi,Xi,Yi,Zi]... - = Murat_image3D(X,Y,Z,mQc,... - turbo,sections,evestaz_Qc,x,y,z,divi,FName_QcMap); - title('Coda attenuation',... - 'FontSize',sizeTitle,'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QcMap); - Murat_saveFigures(Qcmap,pathFolder); - + storeFolder = fullfile('Results','Qc'); + FName_QcMap = ['Qc-3D_' fcName '_Hz']; + [Qcmap,qc_inter,xi,yi,zi,Xi,Yi,Zi] = Murat_image3D(X,Y,Z,mQc,... + turbo,sections,evst_Qc,x,y,z,divi,FName_QcMap); + title('Coda attenuation','FontSize',sTitle,'FontWeight','bold',... + 'Color','k'); + + Murat_saveFigures(Qcmap, makePath(storeFolder, FName_QcMap)); + % interpolated for the parameter map - interp_modv_qc_k = Murat_unfold(Xi,Yi,Zi,qc_inter); + interp_modvQc_k = Murat_unfold(Xi,Yi,Zi,qc_inter); %% % Q results - storeFolder = 'Results/Q'; - FName_QMap = ['Q-3D_' fcName '_Hz']; - Qmap = Murat_image3D(X,Y,Z,mQ,... - hot,sections,evestaz_Q,x,y,z,divi,FName_QMap); - title('Total attenuation variations','FontSize',sizeTitle,... + storeFolder = fullfile('Results','Q'); + FName_QMap = ['Q-3D_' fcName '_Hz']; + Qmap = Murat_image3D(X,Y,Z,mQ,hot,sections,evst_Q,... + x,y,z,divi,FName_QMap); + title('Total attenuation variations','FontSize',sTitle,... 'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QMap); - Murat_saveFigures(Qmap,pathFolder); + Murat_saveFigures(Qmap, makePath(storeFolder, FName_QMap)); + + end - %% PLOT - CHECKERBOARDS - % In this section Murat_plot shows the checkerboard tests - % for Q and Qc. - [~,~,~,check_inputQc] = Murat_fold(x,y,z,modv_Qc_k(:,6)); - [~,~,~,check_outputQc] = Murat_fold(x,y,z,modv_Qc_k(:,7)); - [~,~,~,check_inputQ] = Murat_fold(x,y,z,modv_Qc_k(:,6)); - [~,~,~,check_outputQ] = Murat_fold(x,y,z,modv_Q_k(:,7)); + %CHECKERBOARDS and HIT MAPS + % In this section Murat_plot shows the hit map for peak delays and + % checkerboard tests for Q and Qc. + if PlotCheckers == 1 - %% - % Checkerboard Qc: Input and Output - storeFolder = 'Checkerboard/Qc'; - FName_QcCheck = ['Qc-Checkerboard_' fcName '_Hz']; - Qc_check = figure('Name',FName_QcCheck,... - 'NumberTitle','off','Position',[20,400,2000,1000],'visible','off'); + storeFolder = fullfile('Checkerboard','PD'); + FName_PDC = ['PD-HitMap_' fcName '_Hz']; + rPD = modv_pd_k(:,5); + Murat_hitmap(CoordGrid,rPD,makePath(storeFolder, FName_PDC)); + + storeFolder = fullfile('Checkerboard','Qc'); + + [~,~,~,check_inputQc] = Murat_fold(x,y,z,modv_Qc_k(:,6)); + [~,~,~,check_outputQc] = Murat_fold(x,y,z,modv_Qc_k(:,7)); + [~,~,~,check_inputQ] = Murat_fold(x,y,z,modv_Qc_k(:,6)); + [~,~,~,check_outputQ] = Murat_fold(x,y,z,modv_Q_k(:,7)); + + FName_QcC = ['Qc-Checkerboard_' fcName '_Hz']; + Qc_check = myfig(FName_QcC); subplot(1,2,1) - Murat_image3D_2panels(X,Y,Z,check_inputQc,... - 'bone',sections,evestaz_Qc,x,y,z); - title('Input checkerboard Qc','FontSize',sizeTitle,... + Murat_image3D_2panels(X,Y,Z,check_inputQc,'bone',sections,... + evst_Qc,x,y,z); + title('Input checkerboard Qc','FontSize',sTitle,... 'FontWeight','bold','Color','k'); subplot(1,2,2) Murat_image3D_2panels(X,Y,Z,check_outputQc,... - 'bone',sections,evestaz_Qc,x,y,z); - title('Output checkerboard Qc','FontSize',sizeTitle,... + 'bone',sections,evst_Qc,x,y,z); + title('Output checkerboard Qc','FontSize',sTitle,... 'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QcCheck); - Murat_saveFigures_2panels(Qc_check,pathFolder); + Murat_saveFigures_2panels(Qc_check,makePath(storeFolder,FName_QcC)); - %% %Checkerboard Q: Input and Output - storeFolder = 'Checkerboard/Q'; - FName_QCheck = ['Q-Checkerboard_' fcName '_Hz']; - Q_check = figure('Name',FName_QCheck,... - 'NumberTitle','off','Position',[20,400,2000,1000],'visible','off'); + storeFolder = fullfile('Checkerboard','Q'); + FName_QCheck = ['Q-Checkerboard_' fcName '_Hz']; + Q_check = myfig(FName_QCheck); subplot(1,2,1) Murat_image3D_2panels(X,Y,Z,check_inputQ,... - 'bone',sections,evestaz_Q,x,y,z); - title('Input checkerboard Q','FontSize',sizeTitle,... + 'bone',sections,evst_Q,x,y,z); + title('Input checkerboard Q','FontSize',sTitle,... 'FontWeight','bold','Color','k'); subplot(1,2,2) Murat_image3D_2panels(X,Y,Z,check_outputQ,... - 'bone',sections,evestaz_Q,x,y,z); + 'bone',sections,evst_Q,x,y,z); title('Output checkerboard Q',... - 'FontSize',sizeTitle,'FontWeight','bold','Color','k'); - - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QCheck); - Murat_saveFigures_2panels(Q_check,pathFolder); - - %% PLOT - SPIKES + 'FontSize',sTitle,'FontWeight','bold','Color','k'); + Murat_saveFigures_2panels(Q_check,makePath(storeFolder, FName_QCheck)); + + end + %SPIKES + % In this section Murat_plot shows input and output of the spike tests % for Q and Qc. - [~,~,~,spike_inputQc] = Murat_fold(x,y,z,modv_Qc_k(:,8)); - [~,~,~,spike_outputQc] = Murat_fold(x,y,z,modv_Qc_k(:,9)); - [~,~,~,spike_inputQ] = Murat_fold(x,y,z,modv_Qc_k(:,8)); - [~,~,~,spike_outputQ] = Murat_fold(x,y,z,modv_Q_k(:,9)); - %% + if PlotSpikes == 1 + + [~,~,~,spike_inQc] = Murat_fold(x,y,z,modv_Qc_k(:,8)); + [~,~,~,spike_outQc] = Murat_fold(x,y,z,modv_Qc_k(:,9)); + [~,~,~,spike_inQ] = Murat_fold(x,y,z,modv_Qc_k(:,8)); + [~,~,~,spike_outQ] = Murat_fold(x,y,z,modv_Q_k(:,9)); + % Spike Qc: Input and Output - storeFolder = 'Spike/Qc'; - FName_QcSpike = ['Qc-Spike_' fcName '_Hz']; - Qc_spike = figure('Name',FName_QcSpike,... - 'NumberTitle','off','Position',[20,400,2000,1000],'visible','off'); + storeFolder = fullfile('Spike','Qc'); + FNameQcSpike = ['Qc-Spike_' fcName '_Hz']; + Qc_spike = myfig(FNameQcSpike); subplot(1,2,1) - Murat_image3D_2panels(X,Y,Z,spike_inputQc,... - winter,sections,evestaz_Qc,x,y,z); - title('Input spike Qc','FontSize',sizeTitle,... + Murat_image3D_2panels(X,Y,Z,spike_inQc,winter,sections,evst_Qc,x,y,z); + title('Input spike Qc','FontSize',sTitle,... 'FontWeight','bold','Color','k'); subplot(1,2,2) - Murat_image3D_2panels(X,Y,Z,spike_outputQc,... - winter,sections,evestaz_Qc,x,y,z); - title('Output spike Qc','FontSize',sizeTitle,... + Murat_image3D_2panels(X,Y,Z,spike_outQc,... + winter,sections,evst_Qc,x,y,z); + title('Output spike Qc','FontSize',sTitle,... 'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QcSpike); - Murat_saveFigures_2panels(Qc_spike,pathFolder); + Murat_saveFigures_2panels(Qc_spike,makePath(storeFolder,FNameQcSpike)); - %% % Spike Q: Input and Output - storeFolder = 'Spike/Q'; - FName_QSpike = ['Q-Spike_' fcName '_Hz']; - Q_spike = figure('Name',FName_QSpike,... - 'NumberTitle','off','Position',[20,400,2000,1000],'visible','off'); + storeFolder = fullfile('Spike','Q'); + FNameQSpike = ['Q-Spike_' fcName '_Hz']; + Q_spike = myfig(FNameQSpike); subplot(1,2,1) - Murat_image3D_2panels(X,Y,Z,spike_inputQ,hot,sections,evestaz_Q,x,y,z); - title('Input spike Q','FontSize',sizeTitle,... + Murat_image3D_2panels(X,Y,Z,spike_inQ,hot,sections,evst_Q,x,y,z); + title('Input spike Q','FontSize',sTitle,... 'FontWeight','bold','Color','k'); subplot(1,2,2) - Murat_image3D_2panels(X,Y,Z,spike_outputQ,... - hot,sections,evestaz_Q,x,y,z); - title('Output spike Q','FontSize',sizeTitle,... + Murat_image3D_2panels(X,Y,Z,spike_outQ,... + hot,sections,evst_Q,x,y,z); + title('Output spike Q','FontSize',sTitle,... 'FontWeight','bold','Color','k'); - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_QSpike); - Murat_saveFigures_2panels(Q_spike,pathFolder); - + Murat_saveFigures_2panels(Q_spike,makePath(storeFolder,FNameQSpike)); + end + %% PARAMETER PLOT + if PlotParameters == 0 %%KILLING THIS FOR NOW % The final figure is the parameter plot separation. % First Qc and Peak delay are separated in 4 quadrants. % The second part produces the spatial plot, setting each node to the % corresponding color. The four options are: (1) high for both (red); % (2) low for both (green); (3) high for peak delays only (cyan); % (4) high for inverse Qc only (orange). - storeFolder = 'Results/Parameter'; + storeFolder = fullfile('Results','Parameter'); - %% % Define all the parameters for imaging - FName_Parameters =... - ['Parameter_space_variations_' fcName '_Hz']; - [param_plot,~,~] =... - Murat_imageParameters(x,y,z,modv_pd_k,modv_Qc_k,sizeTitle); - saveas(param_plot,fullfile(FPath,FLabel,storeFolder,FName_Parameters)); + FName_Param = ['Parameter_space_variations_' fcName '_Hz']; + [param_plot,~,~]= Murat_imageParameters(x,y,z,modv_pd_k,... + modv_Qc_k,sTitle); + savefig(param_plot,makePath(storeFolder,FName_Param)); close(param_plot) - % use interpolated peakdelay and Qc - zi = (zi*1000)'; + % Use interpolated peakdelay and Qc + zi = (zi*1000)'; [~,par_inter,para_map_inter] = Murat_imageParameters(xi',yi',... - zi,interp_modv_pd_k,interp_modv_qc_k,sizeTitle); + zi,interp_modv_pd_k,interp_modvQc_k,sTitle); %% % Imaging the parameters in 3D - FName_PMap = ['Parameter-Map_' fcName '_Hz']; + FName_PMap = ['Parameter-Map_' fcName '_Hz']; [ParaMap,para_map] =... Murat_imageParametersMaps(par_inter,para_map_inter,xi',yi',zi,... - Xi,Yi,Zi,evestaz_Qc,sections,sizeTitle,FName_PMap); - - pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_PMap); - Murat_saveFigures(ParaMap,pathFolder); + Xi,Yi,Zi,evst_Qc,sections,sTitle,FName_PMap); + Murat_saveFigures(ParaMap,makePath(storeFolder, FName_PMap)); - FName =... - ['parameterMap_' fcName '_Degrees_Hz.txt']; - writematrix(para_map,fullfile(FPath, FLabel, 'TXT', FName)); + FName = ['parameterMap_' fcName '_Degrees_Hz.txt']; + writematrix(para_map,makePath('TXT',FName)); + + end end -%% % Final figure is the Qc vs frequency relation -storeFolder = 'Tests'; -Murat.Qc.averageQcFrequency = averageQcFrequency; -Qcf_title = 'Qc vs Frequency'; -QcFrequency = Murat_imageQcFrequency(cf,... - averageQcFrequency,sizeTitle,Qcf_title); -FName = 'Qc_vs_frequency'; -saveas(QcFrequency, fullfile(FPath,FLabel,storeFolder,FName),'png'); -close all +Murat.Qc.averageQcFrequency = avQcFreq; +Qcf_title = 'Qc vs Frequency'; +Murat_imageQcFrequency(cf, avQcFreq, sTitle, Qcf_title); +saveFigureAsImage(makePath('Results', 'Qc_vs_frequency')); +close all; + +end \ No newline at end of file diff --git a/bin/Murat_rays.m b/bin/Murat_rays.m index 5c79b07..2a8a70c 100644 --- a/bin/Murat_rays.m +++ b/bin/Murat_rays.m @@ -17,22 +17,25 @@ % rma_i: rays in the three components % rayCrossing_i: hit counts -Apd_i = zeros(1,length(modv(:,1))); -A_i = zeros(1,length(modv(:,1))); +nCells = size(modv,1); -ray = reshape(sst_i,3,2); -rma = Murat_tracing(ray,gridD,pvel); +Apd_i = zeros(1,nCells); +A_i = zeros(1,nCells); + +ray = reshape(sst_i,3,2); +rma = Murat_tracing(ray,gridD,pvel); %Calculation of segment lengths -[lunpar, blocch,... - luntot_i, s, rayCrossing_i] = Murat_segments(modv,rma); +[lunpar, blocch,luntot_i, s, rayCrossing_i] = Murat_segments(modv,rma); + +hitMask = blocch > 0; +cellsHit = blocch(hitMask); -lb = blocch>0; -Apd_i(blocch(lb)) = lunpar(lb)/1000; -A_i(blocch(lb)) = -lunpar(lb)/1000.*s(lb); +Apd_i(cellsHit) = lunpar(hitMask)/1000; +A_i(cellsHit) = -lunpar(hitMask)/1000.*s(hitMask); % Interpolation for plotting rays -lrma = rma(end,1); -in_rma = floor(linspace(1,lrma,100)); -rma_i = rma(in_rma,:); +lrma = rma(end,1); +idx = round(linspace(1, max(1, lrma), 100)); +rma_i = rma(idx, :); end diff --git a/bin/Murat_readLithos1.m b/bin/Murat_readLithos1.m new file mode 100644 index 0000000..e45697c --- /dev/null +++ b/bin/Murat_readLithos1.m @@ -0,0 +1,110 @@ +function modvOriginal = Murat_readLithos1(filename,queryLat,queryLon) +% modvOriginal = Murat_readLithos1(filename,queryLat,queryLon) +% +% READS Lithos1.0 data and builds the 1D model in iaspei91 style, given +% average latitude and longitude +% +% Input parameters: +% filename: name of the velocity model (LITHOS1.0.nc) +% queryLat: average latitude of the model +% queryLon: average longitude of the model +% +% Output parameters: +% modvOriginal: original velocity model in iaspei91 format + +info = ncinfo(filename); +varnames = {info.Variables.Name}; + +lats = ncread(filename, "latitude"); +lons = ncread(filename, "longitude"); + +% Find nearest indices (assumes lats and lons are 1D) +[~, iLat] = min(abs(lats - queryLat)); +[~, iLon] = min(abs(lons - queryLon)); + +% Identify depth variables (names that end with '_depth') +isDepth = endsWith(varnames, "_depth"); +depthVars = varnames(isDepth); + +% Prepare storage +depthValues = zeros(numel(depthVars),1); +baseNames = strings(numel(depthVars),1); + +for k = 1:numel(depthVars) + dname = depthVars{k}; + % read depth matrix and extract scalar at grid cell + try + dmat= ncread(filename, dname); + catch + error("Failed to read variable %s", dname); + end + % dmat may be [lon x lat] or [lat x lon]; choose by matching sizes + % Try both index orders safely: + val = tryIndex(dmat, iLon, iLat, iLat, iLon); + depthValues(k) = val; + base = extractBefore(dname, "_depth"); + baseNames(k) = base; +end + +% Collect properties for each base (other vars that start with base + "_") +propsMap = containers.Map; % key: property name (e.g., 'vp'), value: vector across layers +% also collect depths associated per layer +for k = 1:numel(baseNames) + base = baseNames(k); + depth_k = depthValues(k); + % find variables that start with base + "_" but are not the depth var + prefix = base + "_"; + matches = startsWith(varnames, prefix); + matches = matches & ~isDepth; % exclude the depth var itself + matchedNames = varnames(matches); + for m = 1:numel(matchedNames) + vname = matchedNames{m}; + % property name = suffix after base + '_' + prop = extractAfter(vname, char(prefix)); + % read variable and extract scalar at grid cell + vmat = ncread(filename, vname); + vval = tryIndex(vmat, iLon, iLat, iLat, iLon); + % append (depth, value) pair into a per-property cell array + if ~isKey(propsMap, prop) + propsMap(prop) = [depth_k, vval]; % first row: [depth, value] + else + propsMap(prop) = [propsMap(prop); depth_k, vval]; + end + end +end + +% Now for each property sort by depth and create column vectors +propNames = keys(propsMap); +result = struct(); +for p = 1:numel(propNames) + prop = propNames{p}; + pairs = propsMap(prop); % N x 2: [depth, value] + % remove rows with NaN depth or value + pairs = pairs(~isnan(pairs(:,1)), :); + % sort by depth ascending (shallow -> deep) + [~, idx] = sort(pairs(:,1), 'ascend'); + pairs = pairs(idx, :); + result.(prop).depth = pairs(:,1); + result.(prop).value = pairs(:,2); +end + +modvp = [result.vp.depth 6371-result.vp.depth result.vp.value result.vs.value]; +modvp = sortrows(modvp,[1,3]); +[g, groupVals] = findgroups(modvp(:,1)); +meanCol3 = splitapply(@mean, modvp(:,3), g); +meanCol4 = splitapply(@mean, modvp(:,4), g); + +idx = find(meanCol3 == 0 & (1:numel(meanCol3))' < numel(meanCol3)); +meanCol3(idx) = meanCol3(idx + 1)/2; + +idx = find(meanCol4 == 0 & (1:numel(meanCol4))' < numel(meanCol4)); +meanCol4(idx) = meanCol4(idx + 1)/2; + +modvp1 = [-5 6376 meanCol3(1) meanCol4(1); groupVals, 6371-groupVals, meanCol3, meanCol4]; + +xq = min(modvp1(:,1)):1:max(modvp1(:,1)); + +y3q = interp1(modvp1(:,1), modvp1(:,3), xq, 'pchip', 'extrap'); +y4q = interp1(modvp1(:,1), modvp1(:,4), xq, 'pchip', 'extrap'); + +modvOriginal = [xq' 6371-xq' y3q' y4q']; diff --git a/bin/Murat_rescale.m b/bin/Murat_rescale.m index 797790c..b447dae 100644 --- a/bin/Murat_rescale.m +++ b/bin/Murat_rescale.m @@ -10,14 +10,12 @@ % Output parameters: % modvR: rescaled field -[X,Y,Z] = meshgrid(x,y,z); -mR = griddata(x_o,y_o,z_o,v_o,X,Y,Z); +[X,Y,Z] = meshgrid(x,y,z); +mR = griddata(x_o,y_o,z_o,v_o,X,Y,Z); -if find(isnan(mR)) - mR = inpaintn(mR); +if any(isnan(mR(:))) + mR = inpaintn(mR); end -modvR = Murat_unfold(X,Y,Z,mR); -end - - \ No newline at end of file +modvR = Murat_unfold(X,Y,Z,mR); +end \ No newline at end of file diff --git a/bin/Murat_retainPeakDelay.m b/bin/Murat_retainPeakDelay.m index b62df98..b2a2a1e 100644 --- a/bin/Murat_retainPeakDelay.m +++ b/bin/Murat_retainPeakDelay.m @@ -1,8 +1,7 @@ -function [pab, lpdelta_i, retain_pd_i, ray_crosses_pd_i] ... - = ... +function [pab, lpdelta_i, retain_pd_i, ray_crosses_pd_i] = ... Murat_retainPeakDelay(t_phase, l10pd_i, yesPD_i, Apd_i, stDevPD) % function [pab,lpdelta_i,retain_pd_i,ray_crosses_pd_i] = ... -% Murat_retainPeakDelay(t_phase,l10pd_i,yesPD_i,Apd_i, stDevPD) +% Murat_retainPeakDelay(t_phase,l10pd_i,yesPD_i,Apd_i, stDevPD) % % CREATES all constraints for peak delay inversion % @@ -19,38 +18,28 @@ % retain_pd_i: keeps tab on which waveforms are kept for imaging % ray_crosses_pd_i: keeps tab on which rays are kept for imaging -% Get the length of the input data -dataL = length(t_phase); - -% Initialize outlier flag array -outlierspd = false(dataL, 1); - % Perform robust linear fitting on the data -fitrobust_i = fit(t_phase(yesPD_i), l10pd_i(yesPD_i), 'poly1', 'Robust', 'on'); -pab = [fitrobust_i.p1 fitrobust_i.p2]; +fitr_i = fit(t_phase(yesPD_i), l10pd_i(yesPD_i),'poly1','Robust','on'); +pab = [fitr_i.p1 fitr_i.p2]; % Calculate fitted values and residuals -l10pdt = polyval(pab, t_phase); -lpdelta_i = l10pd_i - l10pdt; +lpdelta_i = l10pd_i - polyval(pab, t_phase); % Standard Deviation (3-sigma) method for outlier detection -mu = mean(lpdelta_i(yesPD_i), 'omitnan'); -sigma = std(lpdelta_i(yesPD_i), 'omitnan'); -lower_bound = mu - stDevPD * sigma; -upper_bound = mu + stDevPD * sigma; +mu = mean(lpdelta_i(yesPD_i), 'omitnan'); +sigma = std(lpdelta_i(yesPD_i), 'omitnan'); -% Flag outliers -I = (lpdelta_i < lower_bound) | (lpdelta_i > upper_bound); +lB = mu - stDevPD * sigma; +uB = mu + stDevPD * sigma; -% Update outlier flags -outlierspd(yesPD_i) = I(yesPD_i); +outliersS = (lpdelta_i(yesPD_i) < lB) | (lpdelta_i(yesPD_i) > uB); % Determine which waveforms to retain -retain_pd_i = yesPD_i & ~outlierspd; +retain_pd_i = false(size(yesPD_i)); +retain_pd_i(yesPD_i)= ~outliersS; -% Process retained waveform data -Apd_retain_pd_i = Apd_i(retain_pd_i, :); -s_pd = sum(Apd_retain_pd_i); -ray_crosses_pd_i = s_pd ~= 0; +% rays crossing retained waveforms: sum over retained rows of Apd_i +s_pd = sum(Apd_i(retain_pd_i, :), 1); +ray_crosses_pd_i = (s_pd ~= 0); end \ No newline at end of file diff --git a/bin/Murat_retainQc.m b/bin/Murat_retainQc.m index 166d616..b3e6d2c 100644 --- a/bin/Murat_retainQc.m +++ b/bin/Murat_retainQc.m @@ -1,5 +1,5 @@ -function [retain_Qm_i,ray_crosses_Qc_i] =... - Murat_retainQc(fT,Qm_i,RZZ_i,Ac_i,QcM) +function retain_Qm_i =... + Murat_retainQc(fT,Qm_i,RZZ_i,QcM) % function [retain_Qm_i,ray_crosses_Qc_i] =... % Murat_retainQc(fT,Qm_i,RZZ_i,Ac_i,QcM) % @@ -20,19 +20,10 @@ retainQmTemp = Qm_i>0 & RZZ_i>fT; retain_Qm_i = Qm_i>0 & RZZ_i>fT &... Qm_i < mean(Qm_i(retainQmTemp))+2*std(Qm_i(retainQmTemp)); - - Ac_retain_Qc_i = Ac_i(retain_Qm_i,:); - Ac_retain_Qc_i(Ac_retain_Qc_i<10^(-4)) = 0; - s_Qc = sum(Ac_retain_Qc_i); - ray_crosses_Qc_i = s_Qc > 0.1; elseif isequal(QcM,'NonLinear') - retain_Qm_i = Qm_i>0; - Ac_retain_Qc_i = Ac_i(retain_Qm_i,:); - Ac_retain_Qc_i(Ac_retain_Qc_i<10^(-4)) = 0; - s_Qc = sum(Ac_retain_Qc_i); - ray_crosses_Qc_i = s_Qc > 0.1; - + retain_Qm_i = Qm_i>0 & RZZ_i0 & rma(:,3)>0); +fallr = find(rma(:,2)>0 & rma(:,3)>0); % Define the interpolated vectors for hit count -xpolo_noint = rma(fallr,2); -ypolo_noint = rma(fallr,3); -zpolo_noint = rma(fallr,4); - -tot = 1000; -xpolo =... - linspace(xpolo_noint(1),xpolo_noint(end),tot); -ypolo =... - linspace(ypolo_noint(1),ypolo_noint(end),tot); -zpolo =... - linspace(zpolo_noint(1),zpolo_noint(end),tot); +xpolo_noint = rma(fallr,2); +ypolo_noint = rma(fallr,3); +zpolo_noint = rma(fallr,4); + +tot = 1000; +xpolo = linspace(xpolo_noint(1),xpolo_noint(end),tot); +ypolo = linspace(ypolo_noint(1),ypolo_noint(end),tot); +zpolo = linspace(zpolo_noint(1),zpolo_noint(end),tot); % Read hypocenter coordinates -sorg = [xpolo(1) ypolo(1) zpolo(1)]; +sorg = [xpolo(1) ypolo(1) zpolo(1)]; % Read station coordinates -ricev = [xpolo(end) ypolo(end) zpolo(end)]; +ricev = [xpolo(end) ypolo(end) zpolo(end)]; -index = 1; -xv = modv(:,1); -yv = modv(:,2); -zv = modv(:,3); +index = 1; +xv = modv(:,1); +yv = modv(:,2); +zv = modv(:,3); % Number corresponding to each block of the grid -bv = 1:length(xv); +bv = 1:length(xv); %% % Find the block corresponding to the source -bS = xv <= sorg(1) & sorg(1) < xv+deltastepx... - & yv <= sorg(2) & sorg(2) < yv+deltastepy... - & zv+deltastepz <= sorg(3) & sorg(3) < zv; +bS = xv <= sorg(1) & sorg(1) < xv+deltastepx... + & yv <= sorg(2) & sorg(2) < yv+deltastepy... + & zv+deltastepz <= sorg(3) & sorg(3) < zv; % This is the block, added +1 as reference is the deepest point to the SW -bSS = bv(bS>0)+1; +bSS = bv(bS>0)+1; if isempty(bSS) - error('Source outside velocity model!!') + error('Source outside of the velocity model!!') end %% % Creation of file inte, which samples the crossing points of the ray -inte = zeros(100,6); -inte(1,1:6) = [sorg(1),sorg(2),sorg(3),0,0,0]; -inte(2,6) = bSS; +inte = zeros(100,6); +inte(1,1:6) = [sorg(1),sorg(2),sorg(3),0,0,0]; +inte(2,6) = bSS; -diffLoc = zeros(tot,1); -k1 = 1; +diffL = zeros(tot,1); +k1 = 1; for k = 2:tot - ewS = xpolo(k-1); - nsS = ypolo(k-1); - vS = zpolo(k-1); - ewR = xpolo(k); - nsR = ypolo(k); - vR = zpolo(k); - - diffLoc(k) = sqrt((ewR-ewS)^2+(nsR-nsS)^2+(vR-vS)^2); + ewS = xpolo(k-1); + nsS = ypolo(k-1); + vS = zpolo(k-1); + ewR = xpolo(k); + nsR = ypolo(k); + vR = zpolo(k); + + diffL(k)= sqrt((ewR-ewS)^2+(nsR-nsS)^2+(vR-vS)^2); - cx = (ewS <= BLx & ewR > BLx) |... - (ewS >=BLx & ewR < BLx); - [a_x, b_x] = find(cx); - cy = (nsS <= BLy & nsR > BLy) |... - (nsS >=BLy & nsR < BLy); - [a_y, b_y] = find(cy); - cv = (vS <= BLv & vR > BLv) |... - (vS >= BLv & vR < BLv); - [a_z, b_z] = find(cv); + cx = (ewS <= BLx & ewR > BLx) | (ewS >=BLx & ewR < BLx); + [a_x, b_x] = find(cx); + cy = (nsS <= BLy & nsR > BLy) | (nsS >=BLy & nsR < BLy); + [a_y, b_y] = find(cy); + cv = (vS <= BLv & vR > BLv) | (vS >= BLv & vR < BLv); + [a_z, b_z] = find(cv); % x crossing if a_x > 0 - index = index + 1; - inte(index,1:5) = [BLx(b_x),nsR,vR,index-1,0]; - lung = sum(diffLoc(k1:k)); - k1 = k; - inte(index,5) = lung; + index = index + 1; + inte(index,1:5) = [BLx(b_x),nsR,vR,index-1,0]; + lung = sum(diffL(k1:k)); + k1 = k; + inte(index,5) = lung; if ewS <= ewR if inte(index,6)+passox < max(bv) - inte(index+1,6) = inte(index,6)+passox; + inte(index+1,6) = inte(index,6)+passox; else break end elseif ewS > ewR if inte(index,6)-passox > 0 - inte(index+1,6) = inte(index,6)-passox; + inte(index+1,6) = inte(index,6)-passox; else break end @@ -157,8 +151,8 @@ if a_y > 0 index = index + 1; inte(index,1:5) = [ewR,BLy(b_y),vR,index-1,0]; - lung = sum(diffLoc(k1:k)); - k1 = k; + lung= sum(diffL(k1:k)); + k1 = k; inte(index,5) = lung; if nsS <= nsR if inte(index,6)+passox < max(bv) @@ -179,18 +173,18 @@ if a_z > 0 index = index + 1; inte(index,1:5) = [ewR,nsR,BLv(b_z),index-1,0]; - lung = sum(diffLoc(k1:k)); + lung = sum(diffL(k1:k)); k1 = k; inte(index,5) = lung; if vS <= vR if inte(index,6)+passoz < max(bv) - inte(index+1,6) = inte(index,6)-passoz; + inte(index+1,6) = inte(index,6)-passoz; else break end elseif vS > vR if inte(index,6)+passoz > 0 - inte(index+1,6) = inte(index,6)+passoz; + inte(index+1,6) = inte(index,6)+passoz; else break end @@ -204,20 +198,20 @@ if k == tot % receiver location - bR = xv < ricev(1) & ricev(1)0); + bR = xv < ricev(1) & ricev(1)0); - if isempty(bRR) == 0 + if ~isempty(bRR) index = index + 1; - lung = sum(diffLoc(k1:end)); + lung = sum(diffL(k1:end)); inte(index,1:5) = [ricev(1),ricev(2),ricev(3),index-1,lung]; end end % Variable inte monitors the points where the rays cross the grid -linte = length(inte(:,1)); +linte = length(inte(:,1)); lunpar(1:linte,1) = inte(:,5); blocch(1:linte,1) = inte(:,6); diff --git a/bin/Murat_selection.m b/bin/Murat_selection.m index d68ee79..ed7ee71 100644 --- a/bin/Murat_selection.m +++ b/bin/Murat_selection.m @@ -1,147 +1,214 @@ %% Seismic attributesare selected and components are considered -function Murat = Murat_selection(Murat) +function Murat = Murat_selection(Murat) % SELECTS inputs and data -components = Murat.input.components; -tresholdnoise = Murat.input.tresholdNoise; -modv = Murat.input.modv; -PorS = Murat.input.POrS; -listaSac = Murat.input.listSac; -maPD = Murat.input.maximumPeakDelay; -miPD = Murat.input.minimumPeakDelay; -fT = Murat.input.fitTresholdLinear; -QcM = Murat.input.QcMeasurement; -stDevPD = Murat.input.stDevPD; - -Apd_i = Murat.PD.inversionMatrixPeakDelay; -A_i = Murat.Q.inversionMatrixQ; -Ac_i = Murat.Qc.inversionMatrixQc; -peakd = Murat.PD.peakDelay; -luntot = Murat.rays.totalLengthRay; -tPS = Murat.rays.theoreticalTime; -evestaz = Murat.rays.locationsDeg; -Qm = Murat.Qc.inverseQc; -RZZ = Murat.Qc.uncertaintyQc; -rapsp = Murat.Q.energyRatioBodyCoda; -rapspcn = Murat.Q.energyRatioCodaNoise; -raysplot = Murat.rays.raysPlot; -tCoda = Murat.Qc.tCoda; - -dataL = size(peakd,1); -dataFreq = size(peakd,2); -modvL = size(modv,1); - -fitrobust = zeros(2,dataFreq); -ray_crosses_pd = false(modvL,dataFreq); -ray_crosses_Qc = false(modvL,dataFreq); -ray_crosses_Q = false(modvL,dataFreq); +s = Murat.input; +FLabel = s.label; +components = s.components; +tresholdnoise = s.tresholdNoise; +modv = s.modv; +PorS = s.POrS; +listaSac = s.listSac; +maPD = s.maximumPeakDelay; +miPD = s.minimumPeakDelay; +fT = s.fitTresholdLinear; +stDevPD = s.stDevPD; +origin = s.origin; +ending = s.end; +x = s.x; +y = s.y; +z = s.z; +QcM = s.QcMeasurement; +cf = s.centralFrequency; + +Declust = s.declustering; + +Apd_i = Murat.PD.inversionMatrixPeakDelay; +A_i = Murat.Q.inversionMatrixQ; +Ac_i = Murat.Qc.inversionMatrixQc; +peakd = Murat.PD.peakDelay; +luntot = Murat.rays.totalLengthRay; +tPS = Murat.rays.theoreticalTime; +locationsDeg = Murat.rays.locationsDeg; +Qm = Murat.Qc.inverseQc; +RZZ = Murat.Qc.uncertaintyQc; +rapsp = Murat.Q.energyRatioBodyCoda; +rapspcn = Murat.Q.energyRatioCodaNoise; +raysplot = Murat.rays.raysPlot; +tCoda = Murat.Qc.tCoda; + +dataL = size(peakd,1); +dataFreq = size(peakd,2); +modvL = size(modv,1); + +fitrobust = zeros(2,dataFreq); +ray_crosses_pd = false(modvL,dataFreq); +ray_crosses_Qc = false(modvL,dataFreq); +ray_crosses_Q = false(modvL,dataFreq); + +if ~isempty(Declust) + disp(['Waveforms used before Qc and Q declustering: ',... + num2str(numel(listaSac))]) + s.locDegOriginal= locationsDeg; + % prepare folder path builder for clustering outputs (used only if Declust) + storeFolder = 'Tests/Clustering'; + baseFolder = fullfile('./', FLabel, storeFolder); + + +end + %% % Warns about problematic data and saves their names and locations -[problemPD,problemQc,problemRZZ,problemQ,~,compMissing,flagWarning]... - =... +[problemPD,problemQc,problemRZZ,problemQ,~,compMissing,flagWarning]=... Murat_dataWarning(listaSac,tresholdnoise,... - maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,components,0,QcM); + maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,rapsp,components,0,QcM); %% % Selects data in case of multiple components -luntot = luntot(1:components:dataL); -time0 = tPS(1:components:dataL); -tCoda = tCoda(1:components:dataL,:); -evestaz = evestaz(1:components:dataL,:); -raysplot = raysplot(:,:,1:components:dataL); -Ac_i = Ac_i(1:components:dataL,:); -Apd_i = Apd_i(1:components:dataL,:); -A_i = A_i(1:components:dataL,:); +idxComp = 1:components:dataL; + +luntot = luntot(idxComp); +time0 = tPS(idxComp); +tCoda = tCoda(idxComp,:); +locationsDeg = locationsDeg(idxComp,:); +raysplot = raysplot(:,:,idxComp); +Ac_i = Ac_i(idxComp,:,:); +Apd_i = Apd_i(idxComp,:); +A_i = A_i(idxComp,:); if components > 1 - [peakd,Qm,RZZ,rapsp,rapspcn] =... - Murat_components(components,peakd,Qm,RZZ,... - rapsp,rapspcn,compMissing); + [peakd,Qm,RZZ,rapsp,rapspcn] = Murat_components(components,peakd,... + Qm,RZZ,rapsp,rapspcn,compMissing); end -[~,~,~,~,yesPD,~,~] =... - Murat_dataWarning(listaSac,tresholdnoise,... - maPD,miPD,fT,peakd,Qm,RZZ,rapspcn,components,flagWarning,QcM); +[~,~,~,~,yesPD,~,~] = Murat_dataWarning(listaSac,tresholdnoise,maPD,... + miPD,fT,peakd,Qm,RZZ,rapspcn,rapsp,components,flagWarning,QcM); %% % Operations to decide weight of each data for the solution % Using Vp/Vs to map max of S waves in the case of P picking -vpvs = sqrt(3); -l10pd = log10(peakd); +vpvs = sqrt(3); +l10pd = log10(peakd); if PorS == 2 - time0 = time0*vpvs; - t_phase = log10(time0); + time0 = time0*vpvs; + t_phase = log10(time0); elseif PorS == 3 - t_phase = log10(time0); + t_phase = log10(time0); end %% % Remove outliers and inversion parameters with little/no sensitivity and % store the remaining indexes for later -dataLMoreComp = size(peakd,1); -lpdelta = zeros(dataLMoreComp,dataFreq); -retain_pd = false(dataLMoreComp,dataFreq); -retain_Qm = false(dataLMoreComp,dataFreq); -retain_Q = false(dataLMoreComp,dataFreq); +dataLMoreComp = size(peakd,1); +lpdelta = zeros(dataLMoreComp,dataFreq); +retain_pd = false(dataLMoreComp,dataFreq); +retain_Qm = false(dataLMoreComp,dataFreq); +retain_Q = false(dataLMoreComp,dataFreq); + for i = 1:dataFreq + cf_k = cf(i); + fcName = num2str(cf_k); + if find(fcName == '.') + fcName(fcName == '.') = '_'; + end % Peak Delays - yesPD_i = yesPD(:,i); - l10pd_i = l10pd(:,i); - [pab,lpdelta_i,retain_pd_i,ray_crosses_pd_i]... - =... + yesPD_i = yesPD(:,i); + l10pd_i = l10pd(:,i); + [pab,lpdelta_i,retain_pd_i,ray_crosses_pd_i]=... Murat_retainPeakDelay(t_phase,l10pd_i,yesPD_i,Apd_i,stDevPD); % Qc - Qm_i = Qm(:,i); - RZZ_i = RZZ(:,i); - [retain_Qm_i,ray_crosses_Qc_i] =... - Murat_retainQc(fT,Qm_i,RZZ_i,Ac_i,QcM); + Qm_i = Qm(:,i); + RZZ_i = RZZ(:,i); + retain_Qc_i = Murat_retainQc(fT,Qm_i,RZZ_i,QcM); + + if ~isempty(Declust) + + indKeepQc = Murat_declustering(origin,ending,x,y,z,... + QcM,RZZ_i,locationsDeg,Declust); + retain_Qc_i = retain_Qc_i & indKeepQc; + + FName_Cluster = ['ClusteringQc_' fcName '_Hz']; + + clustering = Murat_imageDeclustering(... + locationsDeg,locationsDeg(indKeepQc,:),origin,ending,FName_Cluster); + pathFolder = fullfile(baseFolder, FName_Cluster); + saveFigureAsImage(pathFolder); + close(clustering) + + end + Ac_retain_Qc_i = Ac_i(retain_Qc_i,:,i); + ray_crosses_Qc_i = sum(Ac_retain_Qc_i) > 1e-19; % Coda-normalization - retain_Q_t = rapspcn(:,i)>=tresholdnoise; - retain_Q_nn = ~isnan(Qm_i); - retain_Q_i = retain_Q_nn & retain_Q_t; - A_retain_Q_i = A_i(retain_Q_i,:); - ray_crosses_Q_i = sum(A_retain_Q_i)~=0; + retainQCodaNoise = rapspcn(:,i) >= tresholdnoise; + retainQNaN = ~isnan(Qm_i); + retainQPSCoda = rapsp(:,i) >= tresholdnoise; + retain_Q_i =... + retainQNaN & retainQCodaNoise & retainQPSCoda; + + if ~isempty(Declust) + + indKeepQ = Murat_declustering(origin,ending,x,y,z,... + QcM,1./rapspcn(:,i),locationsDeg,Declust); + retain_Q_i = retain_Q_i & indKeepQ; + + FName_Cluster = ['ClusteringQ_' fcName '_Hz']; + clustering = Murat_imageDeclustering(... + locationsDeg,locationsDeg(indKeepQ,:),origin,ending,FName_Cluster); + pathFolder = fullfile(baseFolder, FName_Cluster); + saveFigureAsImage(pathFolder); + close(clustering) + + end - fitrobust(:,i) = pab; - lpdelta(:,i) = lpdelta_i; - retain_pd(:,i) = retain_pd_i; - ray_crosses_pd(:,i) = ray_crosses_pd_i; - retain_Qm(:,i) = retain_Qm_i; - ray_crosses_Qc(:,i) = ray_crosses_Qc_i; - retain_Q(:,i) = retain_Q_i; - ray_crosses_Q(:,i) = ray_crosses_Q_i; + A_retain_Q_i = A_i(retain_Q_i,:); + ray_crosses_Q_i = sum(A_retain_Q_i) ~= 0; + + fitrobust(:,i) = pab; + lpdelta(:,i) = lpdelta_i; + retain_pd(:,i) = retain_pd_i; + ray_crosses_pd(:,i) = ray_crosses_pd_i; + retain_Qm(:,i) = retain_Qc_i; + ray_crosses_Qc(:,i) = ray_crosses_Qc_i; + retain_Q(:,i) = retain_Q_i; + ray_crosses_Q(:,i) = ray_crosses_Q_i; end -Murat.PD.peakDelay = peakd; -Murat.rays.totalLengthRay = luntot; -Murat.Qc.tCoda = tCoda; -Murat.rays.locationsDeg = evestaz; -Murat.Qc.inverseQc = Qm; -Murat.Qc.uncertaintyQc = RZZ; -Murat.PD.problemPD = problemPD; -Murat.Qc.problemQc = problemQc; -Murat.Qc.problemRZZ = problemRZZ; -Murat.Q.problemQ = problemQ; -Murat.Q.energyRatioBodyCoda = rapsp; -Murat.Q.energyRatioCodaNoise = rapspcn; -Murat.rays.raysPlot = raysplot; -Murat.PD.variationPeakDelay = lpdelta; -Murat.rays.travelTime = time0; -Murat.PD.fitrobust = fitrobust; -Murat.PD.retainPeakDelay = retain_pd; -Murat.Qc.retainQc = retain_Qm; -Murat.Q.retainQ = retain_Q; -Murat.PD.raysPeakDelay = ray_crosses_pd; -Murat.Qc.raysQc = ray_crosses_Qc; -Murat.Q.raysQ = ray_crosses_Q; +disp(['Waveforms used after Qc declustering: ', num2str(sum(retain_Qm))]) +disp(['Waveforms used after Q declustering: ', num2str(sum(retain_Q))]) + +Murat.rays.locationsDeg = locationsDeg; +Murat.rays.totalLengthRay = luntot; +Murat.rays.raysPlot = raysplot; +Murat.rays.travelTime = time0; + +Murat.PD.peakDelay = peakd; +Murat.PD.problemPD = problemPD; +Murat.PD.variationPeakDelay = lpdelta; +Murat.PD.fitrobust = fitrobust; +Murat.PD.retainPeakDelay = retain_pd; +Murat.PD.raysPeakDelay = ray_crosses_pd; Murat.PD.inversionMatrixPeakDelay = Apd_i; -Murat.Qc.inversionMatrixQc = Ac_i; -Murat.Q.inversionMatrixQ = A_i; + +Murat.Qc.tCoda = tCoda; +Murat.Qc.inverseQc = Qm; +Murat.Qc.uncertaintyQc = RZZ; +Murat.Qc.problemQc = problemQc; +Murat.Qc.problemRZZ = problemRZZ; +Murat.Qc.raysQc = ray_crosses_Qc; +Murat.Qc.retainQc = retain_Qm; +Murat.Qc.inversionMatrixQc = Ac_i; + +Murat.Q.problemQ = problemQ; +Murat.Q.energyRatioBodyCoda = rapsp; +Murat.Q.energyRatioCodaNoise= rapspcn; +Murat.Q.retainQ = retain_Q; +Murat.Q.raysQ = ray_crosses_Q; +Murat.Q.inversionMatrixQ = A_i; end diff --git a/bin/Murat_sisma.m b/bin/Murat_sisma.m new file mode 100644 index 0000000..df9d582 --- /dev/null +++ b/bin/Murat_sisma.m @@ -0,0 +1,40 @@ +function [tempis,fsisma_i,SAChdr_i,srate_i] = Murat_sisma(cf,listSac_i) +% function [tempis,fsisma_i,SAChdr_i,srate_i] = Murat_sisma(cf,listSac_i) +% +% SEISMOGRAM calculation at all frequencies, outputs properties of seismograms +% +% Input parameters: +% cf: central frequency +% listSac_i: list of SAC files +% +% Output parameters: +% tempis: time vector of seismogram +% fsisma_i: seismogram filtered at different frequencies +% SAChdr_i: SAC header +% srate_i: sampling rate + +lcf = length(cf); +[tempis,sisma,SAChdr_i] = fget_sac(listSac_i); +srate_i = 1/SAChdr_i.times.delta; + +sisma = detrend(sisma,1); +lsis = length(sisma); +tu = tukeywin(lsis,0.05); +tsisma = tu.*sisma; + +fsisma_i = zeros(lsis,lcf); + +if isequal(srate_i,-12345) + error(['Waveform ' listaSac_i 'has no sampling rate!']) +end + +for i = 1:lcf + % Filter creation - in loop for different frequencies + Wn = ([cf(i)-cf(i)/3 cf(i)+cf(i)/3]/srate_i*2); + [z,p,k] = butter(4,Wn,'bandpass'); + [sos,g] = zp2sos(z,p,k); + + fsisma_i(:,i) = filtfilt(sos,g,tsisma); + +end +end diff --git a/bin/Murat_smoothing.m b/bin/Murat_smoothing.m new file mode 100644 index 0000000..872c91b --- /dev/null +++ b/bin/Murat_smoothing.m @@ -0,0 +1,63 @@ +function CovM = Murat_smoothing(coordPrior,smoothValue,sigmaM) +% function CovM = Murat_smoothing(coordPrior,smoothValue,sigmaM) +% +% CREATES smoothing matrix +% +% Input parameters: +% coordPrior: coords for the starting solution +% smoothValue: smoothing value +% sigmaM: variance for models +% Output parameters: +% CovM: smoothing covariance matrix + + +% Prior +% coordPrior is N-by-3: [lat(deg), lon(deg), z(km)] +R = 6371; % Earth mean radius in km + +lat = deg2rad(coordPrior(:,1)); % N-by-1 +lon = deg2rad(coordPrior(:,2)); % N-by-1 +z = coordPrior(:,3)/1000; % N-by-1 (km) + +% pairwise angular differences (N-by-N) +dlat = lat - lat.'; +dlon = lon - lon.'; + +% haversine for great-circle distance (horizontal) +aa = sin(dlat/2).^2 + (cos(lat) .* cos(lat.')).* sin(dlon/2).^2; +cc = 2 * atan2(sqrt(aa), sqrt(1 - aa)); +dh = R * cc; % horizontal surface distance in km (N-by-N) + +% vertical (radial) pairwise differences +dz = z - z.'; % N-by-N in km + +% 3-D straight-line distances +Dkm = sqrt(dh.^2 + dz.^2); + +% Setting smoothing parameter +if ~isempty(smoothValue) + smoothValue = abs(smoothValue); +else + v = coordPrior(:,3); + ref = v(1); + d = abs(v - ref); + d(v == ref) = Inf; + + idx = find(d == min(d)); + nearestVals = v(idx); + nearVal1 = nearestVals(idx(1)); + smoothValue = abs(3*(nearVal1-ref))/1000; +end + +% CovM: if N is large this is expensive; build sparse approximation if needed +CovM_full = sigmaM * exp(-0.5*(Dkm.^2) / (2*smoothValue^2)); +N = size(coordPrior,1); + +% If N large, consider thresholding to sparse: +if N > 2000 + % keep only neighbors within 3*smoothValue + mask = Dkm <= 3*smoothValue; + CovM = sparse(double(mask) .* CovM_full); +else + CovM = CovM_full; +end \ No newline at end of file diff --git a/bin/Murat_test.m b/bin/Murat_test.m index 13b2dee..b464e8a 100644 --- a/bin/Murat_test.m +++ b/bin/Murat_test.m @@ -16,30 +16,29 @@ % % Imports SAC files -[times,sisma,SAChdr] = fget_sac(nameWaveform); -image = []; +[times,sisma,SAChdr] = fget_sac(nameWaveform); +image = []; %% Figure if figOutput == 1 - srate_i = 1/SAChdr.times.delta; + srate_i = 1/SAChdr.times.delta; - sisma = detrend(sisma,1); + sisma = detrend(sisma,1); - lsis = length(sisma); + lsis = length(sisma); - tu = tukeywin(lsis,0.05); - tsisma = tu.*sisma; + tu = tukeywin(lsis,0.05); + tsisma = tu.*sisma; - image = figure('Name',['Test Seismograms: '... - nameWaveform],'NumberTitle','off','Position',[20,400,1200,1000],... - 'visible','off'); + image = myfig(['Test Seismograms: ' nameWaveform]); + lengthFrequencies = length(centralFrequencies); plotFrequencies = 1:2:2*lengthFrequencies; if isequal(centralFrequencies,[]) plot(times,sisma,'k-','LineWidth',2); xlim([SAChdr.times.a - 5 SAChdr.times.a + 20]) - SetFDefaults + SetFDefaults() else for i = 1:lengthFrequencies % Filter creation - in loop for each frequency @@ -57,13 +56,13 @@ plot(times,fsisma,'k-','LineWidth',2); xlabel('Time (s)') ylabel('Amplitude') - SetFDefaults + SetFDefaults() subplot(lengthFrequencies,2,plotFrequencies(i)+1); plot(times,sp_i,'k-','LineWidth',2); xlabel('Time (s)') ylabel('Energy') - SetFDefaults + SetFDefaults() end end end @@ -137,10 +136,10 @@ end function SetFDefaults() % DEFAULT settings for MuRAt figures -ax = gca; -ax.GridColor = [0 0 0]; -ax.GridLineStyle = '--'; -ax.GridAlpha = 0.3; -ax.LineWidth = 1.5; -ax.FontSize = 12; +ax = gca; +ax.GridColor = [0 0 0]; +ax.GridLineStyle= '--'; +ax.GridAlpha = 0.3; +ax.LineWidth = 1.5; +ax.FontSize = 12; grid on \ No newline at end of file diff --git a/bin/Murat_testData.m b/bin/Murat_testData.m index b0bc1f5..da70a1b 100644 --- a/bin/Murat_testData.m +++ b/bin/Murat_testData.m @@ -1,4 +1,4 @@ -function [muratHeader,flag] =... +function [muratHeader,flag,sacHeader] =... Murat_testData(folderPath,originTime,PTime,STime) % TEST all seismograms in a folder for the input parameters and % CREATES a file storing the parameters and flagging those missing @@ -10,111 +10,136 @@ % STime: S time variable selected by the user % % Output: +% SACheader: SAC headers for all files; % muratHeader: Murat table showing the necessary parameter % flag: flags missing optional variables % -[Names,~] = createsList(folderPath); -lengthData = length(Names); -Origin = cell(lengthData,1); -P = cell(lengthData,1); -S = cell(lengthData,1); -EvLat = cell(lengthData,1); -EvLon = cell(lengthData,1); -EvDepth = cell(lengthData,1); -StLat = cell(lengthData,1); -StLon = cell(lengthData,1); -StElev = cell(lengthData,1); -flag = []; -for i=1:lengthData - listSac_i = Names{i}; - [~,SAChdr] = Murat_test(listSac_i,[],8,0,0); +Names = createsList(folderPath); +nFiles = numel(Names); +Origin = cell(nFiles,1); +P = cell(nFiles,1); +S = cell(nFiles,1); +EvLat = cell(nFiles,1); +EvLon = cell(nFiles,1); +EvDepth = cell(nFiles,1); +StLat = cell(nFiles,1); +StLon = cell(nFiles,1); +StElev = cell(nFiles,1); + +origMissing = false; +sMissing = false; +sacHeader = struct(); + +for i=1:nFiles + fname = Names{i}; + [~,SAChdr] = Murat_test(fname,[],8,0,0); + fld = sprintf('SAC_%g', i); + sacHeader.(fld) = SAChdr; + + originVal = getFieldValue(SAChdr, originTime); if isequal(eval(originTime),-12345) - Origin{i} = []; - flag = 1; + Origin{i} = []; + origMissing = true; else - Origin{i} = eval(originTime); + Origin{i} = originVal; end + pVal = getFieldValue(SAChdr, PTime); if isequal(eval(PTime),-12345) - P{i} = []; - warning(['There is a missing P-value, check ' listSac_i '!']); + error(['There is a missing P value, check ' fname '!']); else - P{i} = eval(PTime); + P{i} = pVal; end - if isequal(eval(STime),-12345) - S{i} = []; - flag = 2; + sVal = getFieldValue(SAChdr, STime); + if isequal(eval(STime),-12345) + S{i} = []; + sMissing = true; else - S{i} = eval(STime); + S{i} = sVal; end if isequal(SAChdr.event.evla,-12345) - EvLat{i} = []; - warning(['There is a missing event coordinate, check '... - listSac_i '!']); + error(['There is a missing event coordinate, check ' fname '!']); else - EvLat{i} = SAChdr.event.evla; + EvLat{i} = SAChdr.event.evla; end if isequal(SAChdr.event.evlo,-12345) - EvLon{i} = []; - warning(['There is a missing event coordinate, check '... - listSac_i '!']); + error(['There is a missing event coordinate, check ' fname '!']); else - EvLon{i} = SAChdr.event.evlo; + EvLon{i} = SAChdr.event.evlo; end if isequal(SAChdr.event.evdp,-12345) - EvDepth{i} = []; - warning(['There is a missing event coordinate, check '... - listSac_i '!']); + error(['There is a missing event coordinate, check ' fname '!']); else - EvDepth{i} = SAChdr.event.evdp; + EvDepth{i} = SAChdr.event.evdp; end - if isequal(SAChdr.station.stla,-12345) - StLat{i} = []; - warning(['There is a missing station coordinate, check '... - listSac_i '!']); + if isequal(SAChdr.station.stla, -12345) + error(['There is a missing station coordinate, check ' fname '!']); else - StLat{i} = SAChdr.station.stla; + StLat{i} = SAChdr.station.stla; end if isequal(SAChdr.station.stlo,-12345) - StLon{i} = []; - warning(['There is a missing station coordinate, check '... - listSac_i '!']); + error(['There is a missing station coordinate, check ' fname '!']); else - StLon{i} = SAChdr.station.stlo; + StLon{i} = SAChdr.station.stlo; end if isequal(SAChdr.station.stel,-12345) - StElev{i} = []; - warning(['There is a missing station coordinate, check '... - listSac_i '!']); + error(['There is a missing station coordinate, check ' fname '!']); else - StElev{i} = SAChdr.station.stel; + StElev{i} = SAChdr.station.stel; end end -muratHeader = table(Names,Origin,P,S,EvLat,EvLon,... +muratHeader = table(Names,Origin,P,S,EvLat,EvLon,... EvDepth,StLat,StLon,StElev); +flag = origMissing + 2*sMissing; + end -%% -function [listWithFolder,listNoFolder]... - = createsList(directory) -% CREATES a list of visible files in a folder, outputs both with and -% without folder -list = dir(directory); -list = list(~startsWith({list.name}, '.')); +%% Helper: Safe nested field read from SAChdr using dot-separated path +function val = getFieldValue(SAChdr, pathStr) +% pathStr examples: 'SAChdr.times.o' or 'SAChdr.times.t0' +% We ignore leading "SAChdr." if present. +if startsWith(pathStr, 'SAChdr.') + pathStr = pathStr(8:end); +end +parts = strsplit(pathStr, '.'); +val = SAChdr; +for k = 1:numel(parts) + fld = parts{k}; + if isstruct(val) && isfield(val, fld) + val = val.(fld); + else + val = []; % missing field -> return empty (not -12345) + return + end +end +end -listWithFolder = fullfile({list.folder},{list.name})'; -listNoFolder = {list.name}'; +%% Helper: createsList +function [listWithFolder, listNoFolder] = createsList(directory) +d = dir(directory); +if isempty(d) + listWithFolder = {}; + listNoFolder = {}; + return +end +names = {d.name}.'; +folders = {d.folder}.'; +mask = ~startsWith(names, '.'); +names = names(mask); +folders = folders(mask); +listWithFolder = fullfile(folders, names); +listNoFolder = names; end \ No newline at end of file diff --git a/bin/Murat_test_vel_models.m b/bin/Murat_test_vel_models.m index e4188b7..45859c6 100644 --- a/bin/Murat_test_vel_models.m +++ b/bin/Murat_test_vel_models.m @@ -1,8 +1,8 @@ -function testModv =... -Murat_test_vel_models(FPath,FLabel,modvOriginal,modvOrCartesian,... - XEqSpace,YEqSpace,ZEqSpace,modvEqS,Xq,Yq,Zq,modvP) -% function [modvP,modvI,modvIP,pvel] =... -% Murat_modv3D(modvXYZ,modvOriginal,origin,flagTest) +function testModv = Murat_test_vel_models(FPath,FLabel,modvOriginal,... + modvOrCartesian,XEqSpace,YEqSpace,ZEqSpace,modvEqS,Xq,Yq,Zq,modvP) +% function testModv = Murat_test_vel_models(FPath,FLabel,modvOriginal,... +% modvOrCartesian,XEqSpace,YEqSpace,ZEqSpace,modvEqS,Xq,Yq,Zq,modvP) +% % TESTS correct recovery of velocity model when using a 3D as input % Input parameters % modvOriginal: original velocity model @@ -16,41 +16,39 @@ % testModv: single figure showing original, cartesian, % equally-spaced and propagation velocity model -testModv = figure('Name','3DVelocityTest',... - 'NumberTitle','off','Position',[20,400,1200,1000],'visible','off'); +testModv = myfig('3DVelocityTest'); % first look at original model % use min and maximum lat / lon of model -lat_res = unique(modvOriginal(:,1)); -lon_res = unique(modvOriginal(:,2)); -depth_res = unique(modvOriginal(:,3)); +lat_res = unique(modvOriginal(:,1)); +lon_res = unique(modvOriginal(:,2)); +depth_res = unique(modvOriginal(:,3)); -sliceLat = mean(lat_res); -sliceLon = mean(lon_res); -sliceDepth = mean(depth_res); +sliceLat = mean(lat_res); +sliceLon = mean(lon_res); +sliceDepth = mean(depth_res); %3D -[XOrModel,YOrModel,ZOrModel]= meshgrid(lon_res,lat_res,depth_res); -VOrModel = griddata(modvOriginal(:,2),... - modvOriginal(:,1),modvOriginal(:,3),modvOriginal(:,4),... - XOrModel,YOrModel,ZOrModel); +[XOrModel,YOrModel,ZOrModel] = meshgrid(lon_res,lat_res,depth_res); +VOrModel = griddata(modvOriginal(:,2),modvOriginal(:,1),... + modvOriginal(:,3),modvOriginal(:,4),XOrModel,YOrModel,ZOrModel); -[color] = inferno(100); +[color] = inferno(100); subplot(2,2,1) slice(XOrModel,YOrModel,ZOrModel, VOrModel, sliceLon, sliceLat, sliceDepth) colormap(color) view(0,90) -hcb = colorbar; +hcb = colorbar; title(hcb,'V (km/s)') title('Original velocity model') %% check out centered model % use min and maximum lon / lat of model -lon_res2 = unique(modvOrCartesian(:,1)); -lat_res2 = unique(modvOrCartesian(:,2)); -depth_res2 = unique(modvOrCartesian(:,3)); +lon_res2 = unique(modvOrCartesian(:,1)); +lat_res2 = unique(modvOrCartesian(:,2)); +depth_res2 = unique(modvOrCartesian(:,3)); %3D [Xmodv_o,Ymodv_o,Zmodv_o] = meshgrid(lon_res2,lat_res2,depth_res2); @@ -58,15 +56,15 @@ modvOrCartesian(:,2),modvOrCartesian(:,3),modvOrCartesian(:,4),... Xmodv_o,Ymodv_o,Zmodv_o); -sliceY = mean(lat_res2); -sliceX = mean(lon_res2); -sliceZ = mean(depth_res2); +sliceY = mean(lat_res2); +sliceX = mean(lon_res2); +sliceZ = mean(depth_res2); subplot(2,2,2) slice(Xmodv_o,Ymodv_o,Zmodv_o, mVmodv_o,sliceX,sliceY,sliceZ) colormap(color) view(0,90) -hcb = colorbar; +hcb = colorbar; title(hcb,'V (km/s)') title('Cartesian velocity model') @@ -76,7 +74,7 @@ slice(XEqSpace,YEqSpace,ZEqSpace,modvEqS,sliceX,sliceY,sliceZ) colormap(color) view(0,90) -hcb = colorbar; +hcb = colorbar; title(hcb,'V (km/s)') title('Equally-spaced velocity model') @@ -87,14 +85,13 @@ slice(Xq,Yq,Zq,modvP,sliceY,sliceX,sliceZ) colormap(color) view(0,90) -hcb = colorbar; +hcb = colorbar; title(hcb,'V (km/s)') title('Propagation velocity model') -storeFolder = 'Tests'; -FName_testV = 'VTest'; -pathFolder =... - fullfile(FPath,FLabel,storeFolder,FName_testV); -saveas(testModv, pathFolder); +storeFolder = 'Tests'; +FName_testV = 'VTest'; +pathFolder = fullfile(FPath,FLabel,storeFolder,FName_testV); +savefig(testModv, pathFolder); end \ No newline at end of file diff --git a/bin/Murat_tikhonovQ.m b/bin/Murat_tikhonovQ.m index bf383b8..f1c295e 100644 --- a/bin/Murat_tikhonovQ.m +++ b/bin/Murat_tikhonovQ.m @@ -1,56 +1,44 @@ -function [mtik0,residualQ_k,LcCN,tik0_reg]... - =... - Murat_tikhonovQ(outputLCurve,A,d1,lCurveQ_k,flagShow) -% function [mtik0,residualQ_k,LcCN,tik0_reg,d1k,constQmean_k]... -% =... -% Murat_tikhonovQ(cfk,rtQ,outputLCurve,rapsp_k,const_Qc_k,... -% luntot,time0,A,lCurveQ,flagShow) +function [mtik0,residualQ_k,LcCN] =... + Murat_tikhonovQ(outputLCurve,A,d1,dampValue,x0) +% function [mtik0,residualQ_k,LcCN]=... +% Murat_tikhonovQ(outputLCurve,A,d1,dampValue) % % INVERTS with weighted tikhonov and creates L-curve and data for Q. % % Input parameters: -% cfk: central frequency -% rtQ: selected Q waveforms % outputLCurve: flag to output the L curve -% rapsp_k: energy ratios -% const_Qc_k: constant from the average Qc -% luntot: total length -% time0: travel time % A: CN inversion matrix -% lCurveQ_k: damping parameter input from start -% flagShow: flag to decide if show L-curve +% d1: weighted data +% dampValue: damping parameter % % Output parameters: % mtik0: inversion parameter % residualQ_k: residuals for Q inversion % LcCN: figure of L curve for the CN method -% tik0_reg: regularization parameters -% d1: data of the inversion -% constQmean_k: constant for the method -%% -% Creates L-curve figure as before and sets damping -[U,S,V] = svd(A); -s = diag(S); +[U,S,~] = svd(A); -if flagShow == 1 - LcCN =... - figure('Name','L-curve Q','NumberTitle','off','visible','off'); - [rho,eta,reg_param] =... - l_curve_tikh_svd(U,s,d1,100); - plot_lc(rho,eta,'-',1,reg_param) - - if outputLCurve == 1 - tik0_reg =... - input('Your damping parameter for Q: '); - else - tik0_reg = lCurveQ_k; - end +if outputLCurve == 1 + visibility = 'on'; else - tik0_reg = lCurveQ_k; - LcCN = 0; + visibility = 'off'; end -mtik0 = tikhonov(U,s,V,d1,tik0_reg); -residualQ_k = sum(abs(d1-A*mtik0).^2); + +LcCN = figure('Name','L-curve Q','NumberTitle','off','visible',visibility); + +[rho,eta,reg_param] = l_curve_tikh_svd(U,diag(S),d1,100); +plot_lc(rho,eta,'-',1,reg_param) +xlabel('Regularization Parameter (rho)'); +ylabel('Residual Norm (eta)'); +grid on; + +obj0 = norm((d1-A*x0).^2); +Gaug = [A; dampValue*eye(numel(x0))]; +baug = [d1; dampValue*x0]; +mtik0 = lsqnonneg(Gaug, baug); +obj = norm((d1-A*mtik0).^2); + +%mtik0 = tikhonov(U,diag(S),V,d1,dampValue); +residualQ_k = obj/obj0; end \ No newline at end of file diff --git a/bin/Murat_tikhonovQc.m b/bin/Murat_tikhonovQc.m index 6c6b6d4..a34a42f 100644 --- a/bin/Murat_tikhonovQc.m +++ b/bin/Murat_tikhonovQc.m @@ -1,40 +1,44 @@ -function [mtik0C,residualQc_k,LcQc,tik0_regC] =... - Murat_tikhonovQc(outputLCurve,Gc,bQm,lCurveQc_k) -% function [mtik0C,residualQc_k,LcQc,tik0_regC] =... -% Murat_tikhonovQc(outputLCurve,Gc,bQm,lCurveQc) +function [mtik0C,residualQc_k,LcQc] =... + Murat_tikhonovQc(outputLCurve,Gc,bQm,dampValue,x0) +% function [mtik0C,residualQc_k,LcQc] =... +% Murat_tikhonovQc(outputLCurve,Gc,bQm,dampValue) % % INVERTS with weighted tikhonov and creates L-curve and data for Qc. % % Input parameters: % outputLCurve: flag to output the L curve -% Qm_k: inverse Qc values -% Wc: weighting matrix -% Gc: inversion matrix -% lCurveQc_k: damping parameter input from start +% Gc: weighted inversion matrix +% bQm: weighted inverse Qc values +% dampValue: damping parameter % % Output parameters: % mtik0C: inversion parameter % residualQc_k: residuals for Qc inversion % LcQc: figure of L curve for the Qc method -% tik0_regC: regularization parameters -[Uc,Sc,Vc] = svd(Gc); - -LcQc =... - figure('Name','L-curve Qc','NumberTitle','off','visible','off'); -[rho,eta,reg_param] =... - l_curve_tikh_svd(Uc,diag(Sc),bQm,100); -plot_lc(rho,eta,'-',1,reg_param) +[Uc,Sc,~] = svd(Gc); if outputLCurve == 1 - tik0_regC =... - input('Your damping parameter for coda: '); + visibility = 'on'; else - tik0_regC = lCurveQc_k; + visibility = 'off'; end -mtik0C =... - tikhonov(Uc,diag(Sc),Vc,bQm,tik0_regC); -residualQc_k =... - sum(abs(bQm-Gc*mtik0C).^2); +LcQc = figure('Name','L-curve Qc','NumberTitle','off','visible',visibility); + +[rho,eta,reg_param] = l_curve_tikh_svd(Uc,diag(Sc),bQm,100); +plot_lc(rho,eta,'-',1,reg_param) +xlabel('Regularization Parameter (rho)'); +ylabel('Residual Norm (eta)'); +grid on; + +obj0 = norm((bQm-Gc*x0).^2); +Gaug = [Gc; dampValue*eye(numel(x0))]; +baug = [bQm; dampValue*x0]; +mtik0C = lsqnonneg(Gaug, baug); +obj = norm((bQm-Gc*mtik0C).^2); + +%mtik0C = tikhonov(Uc,diag(Sc),Vc,bQm,dampValue); +residualQc_k = obj/obj0; + end \ No newline at end of file diff --git a/bin/Murat_tracing.m b/bin/Murat_tracing.m index 3c9f52e..04f454b 100644 --- a/bin/Murat_tracing.m +++ b/bin/Murat_tracing.m @@ -1,5 +1,5 @@ -function ma = Murat_tracing(ray,gridP,pvel) -% function ma = Murat_tracing(ray,gridP,pvel) +function ma = Murat_tracing(ray,gridP,pvel) +% function ma = Murat_tracing(ray,gridP,pvel) % % TRACES the minimum path between source and receiver using pseudo-bending % @@ -18,106 +18,104 @@ % the changes are below a certain treshold. % Maximum number of iterations allowed -maxit = 100; +maxit = 100; % Max bend points, 1 direction -maxpoints = 10000; +maxpoints = 10000; % Three dimensions -max3 = 3*maxpoints; +max3 = 3*maxpoints; % Nodes of the grid -xNodes = gridP.x; -yNodes = gridP.y; -zNodes = gridP.z; +xNodes = gridP.x; +yNodes = gridP.y; +zNodes = gridP.z; % Max distance one cell -dconv = sqrt((xNodes(2)-xNodes(1))^2 +... +dconv = sqrt((xNodes(2)-xNodes(1))^2 +... (yNodes(2)-yNodes(1))^2 + (zNodes(2)-zNodes(1))^2); % Time limit, in seconds, pvel is in km -tlim = dconv/mean(mean(mean(pvel*1000))); +tlim = dconv/mean(mean(mean(pvel*1000))); % Start bending procedure from the extrema of the ray segment -xtemp = [ray(1,1) (ray(1,1)+ray(1,2))/2 ray(1,2)]; -ytemp = [ray(2,1) (ray(2,1)+ray(2,2))/2 ray(2,2)]; -ztemp = [ray(3,1) (ray(3,1)+ray(3,2))/2 ray(3,2)]; +xtemp = [ray(1,1) (ray(1,1)+ray(1,2))/2 ray(1,2)]; +ytemp = [ray(2,1) (ray(2,1)+ray(2,2))/2 ray(2,2)]; +ztemp = [ray(3,1) (ray(3,1)+ray(3,2))/2 ray(3,2)]; -n = 3; -v = zeros(n,1); +n = 3; +v = zeros(n,1); for i=1:n - xp = xtemp(i); - yp = ytemp(i); - zp = ztemp(i); - vp = Murat_velocity(xp,yp,zp,gridP,pvel); - v(i,1) = vp; + v(i) = Murat_velocity(xtemp(i), ytemp(i), ztemp(i), gridP, pvel); end % Computes travel time -[ta,tra] =... - Murat_traveling(xtemp,ytemp,ztemp,gridP,v,pvel); +[ta,tra] = Murat_traveling(xtemp,ytemp,ztemp,gridP,v,pvel); +ttsave = 0; -ttsave = 0; -for j=1:maxit - taa = tra; +for it = 1:maxit + prevT = tra; - [xtemp,ytemp,ztemp,v] =... - Murat_bending(xtemp,ytemp,ztemp,gridP,v,pvel); + [xtemp,ytemp,ztemp,v] = Murat_bending(xtemp,ytemp,ztemp,gridP,v,pvel); - [ta,tra] =... - Murat_traveling(xtemp,ytemp,ztemp,gridP,v,pvel); + [ta,tra] = Murat_traveling(xtemp,ytemp,ztemp,gridP,v,pvel); - deltat = taa-tra; + deltat = prevT-tra; - % Check for convergence if deltat < tlim - dtemp =... - sqrt((xtemp(2)-xtemp(1))^2 + (ytemp(2)-ytemp(1))^2 ... + % Check convergence and distance criterium + dtemp = sqrt((xtemp(2)-xtemp(1))^2 + (ytemp(2)-ytemp(1))^2 ... + (ztemp(2)-ztemp(1))^2); - if abs(ttsave-tra) < tlim && dtemp < dconv + if abs(ttsave-tra) < tlim && dtemp < dconv/10 break else % As the travel time decrease is smaller than travel-time % treshold (tlim) and/or the travel time increased, the % code doubles the number of segments: - ttsave = tra; - ntemp = 2*n-1; - if(ntemp > max3) + ttsave = tra; + nnew = 2*n-1; + if(nnew > max3) error('Too many points, reduce them or increase max3'); - - end - - ta(1)=0; - for i=1:n - l = n-i+1; - k = 2*l-1; - xtemp(k) = xtemp(l); - ytemp(k) = ytemp(l); - ztemp(k) = ztemp(l); - ta(k) = ta(l); - end - - for i=2:2:(2*n-2) - xtemp(i) = (xtemp(i-1)+xtemp(i+1))/2; - ytemp(i) = (ytemp(i-1)+ytemp(i+1))/2; - ztemp(i) = (ztemp(i-1)+ztemp(i+1))/2; end - n = 2*n-1; - for i=1:n - xp = xtemp(i); - yp = ytemp(i); - zp = ztemp(i); - vp = Murat_velocity(xp,yp,zp,gridP,pvel); - v(i) = vp; + % allocate new arrays + xt = zeros(1,nnew); + yt = zeros(1,nnew); + zt = zeros(1,nnew); + + % place original points into odd indices (1,3,5,...) + oddIdx = 1:2:nnew; + xt(oddIdx) = xtemp; + yt(oddIdx) = ytemp; + zt(oddIdx) = ztemp; + + % fill midpoints into even indices + evenIdx = 2:2:(nnew-1); + xt(evenIdx) = 0.5 * (xt(oddIdx(1:end-1)) + xt(oddIdx(2:end))); + yt(evenIdx) = 0.5 * (yt(oddIdx(1:end-1)) + yt(oddIdx(2:end))); + zt(evenIdx) = 0.5 * (zt(oddIdx(1:end-1)) + zt(oddIdx(2:end))); + + xtemp = xt; ytemp = yt; ztemp = zt; + n = nnew; + + % recompute velocities at all nodes + v = zeros(n,1); + for i = 1:n + v(i)=Murat_velocity(xtemp(i),ytemp(i),ztemp(i),gridP,pvel); end + % reset ta so next Murat_traveling recalculates correctly + ta = zeros(1,n); end end end - -% Final ray, including travel time info -ma(1:n,1:5) = [(1:n)',xtemp',ytemp',ztemp',ta'/1000]; +% assemble output: columns [index, x, y, z, travel_time_seconds] +ma = zeros(n,5); +ma(:,1) = (1:n)'; +ma(:,2) = xtemp(:); +ma(:,3) = ytemp(:); +ma(:,4) = ztemp(:); +ma(:,5) = ta(:)' / 1000; % preserve original division by 1000 (units) end \ No newline at end of file diff --git a/bin/Murat_traveling.m b/bin/Murat_traveling.m index 28bc02f..1cc1c69 100644 --- a/bin/Murat_traveling.m +++ b/bin/Murat_traveling.m @@ -1,4 +1,4 @@ -function [ta,tra] = Murat_traveling(xtemp,ytemp,ztemp,gridD,v,pvel) +function [ta,tra] = Murat_traveling(xtemp,ytemp,ztemp,gridD,v,pvel) % function [ta,tra] = Murat_traveling(xtemp,ytemp,ztemp,gridD,v,pvel) % % COMPUTES travel time between bent points. @@ -16,56 +16,56 @@ % tra: travel time % Coordinates of the propagation grid are unwrapped -xGrid = gridD.x; -yGrid = gridD.y; -zGrid = gridD.z; +xGrid = gridD.x; +yGrid = gridD.y; +zGrid = gridD.z; -maxGrid = max([xGrid(2)-xGrid(1)... - yGrid(2)-yGrid(1) zGrid(2)-zGrid(1)]); +maxGrid = max([xGrid(2)-xGrid(1) yGrid(2)-yGrid(1) zGrid(2)-zGrid(1)]); % Max travel distance limit -distlim = 5*(maxGrid); -pvel = pvel*1000; +distlim = 5*(maxGrid); +pvel = pvel*1000; -n = length(xtemp); -xd = xtemp(2)-xtemp(1); -yd = ytemp(2)-ytemp(1); -zd = ztemp(2)-ztemp(1); -ds = sqrt(xd^2+yd^2+zd^2); +n = numel(xtemp); + +xd = xtemp(2)-xtemp(1); +yd = ytemp(2)-ytemp(1); +zd = ztemp(2)-ztemp(1); +ds = sqrt(xd^2+yd^2+zd^2); if ds <= distlim - tra = ds*((1/v(1)+1/v(2))/2); - ta(2) = tra; + tra = ds*((1/v(1)+1/v(2))/2); + ta(2) = tra; for i = 3:n - i1 = i-1; - xd = xtemp(i)-xtemp(i1); - yd = ytemp(i)-ytemp(i1); - zd = ztemp(i)-ztemp(i1); - ds = sqrt(xd^2+yd^2+zd^2); - tra = tra+ds/((v(i)+v(i1))/2); - ta(i) = tra; + i1 = i-1; + xd = xtemp(i)-xtemp(i1); + yd = ytemp(i)-ytemp(i1); + zd = ztemp(i)-ztemp(i1); + ds = sqrt(xd^2+yd^2+zd^2); + tra = tra+ds/((v(i)+v(i1))/2); + ta(i) = tra; end else - tra = 0; - ta = zeros(1,n); + tra = 0; + ta = zeros(1,n); for i = 2:n - i1 = i-1; - xd = xtemp(i)-xtemp(i1); - yd = ytemp(i)-ytemp(i1); - zd = ztemp(i)-ztemp(i1); - ds = sqrt(xd^2+yd^2+zd^2); - xcos = xd/ds; - ycos = yd/ds; - zcos = zd/ds; - is = fix(ds/distlim); - ds = ds/is; + i1 = i-1; + xd = xtemp(i)-xtemp(i1); + yd = ytemp(i)-ytemp(i1); + zd = ztemp(i)-ztemp(i1); + ds = sqrt(xd^2+yd^2+zd^2); + xcos = xd/ds; + ycos = yd/ds; + zcos = zd/ds; + is = fix(ds/distlim); + ds = ds/is; for j = 1:is - xx = xtemp(i1)+(j-0.5)*ds*xcos; - yy = ytemp(i1)+(j-0.5)*ds*ycos; - zz = ztemp(i1)+(j-0.5)*ds*zcos; - vp = Murat_velocity(xx,yy,zz,gridD,pvel); - tra = tra+ds/vp; + xx = xtemp(i1)+(j-0.5)*ds*xcos; + yy = ytemp(i1)+(j-0.5)*ds*ycos; + zz = ztemp(i1)+(j-0.5)*ds*zcos; + vp = Murat_velocity(xx,yy,zz,gridD,pvel); + tra = tra+ds/vp; end - ta(i) = tra; + ta(i) = tra; end end end \ No newline at end of file diff --git a/bin/Murat_unfold.m b/bin/Murat_unfold.m index bb8f8b3..1ddc59b 100644 --- a/bin/Murat_unfold.m +++ b/bin/Murat_unfold.m @@ -1,4 +1,4 @@ -function modv = Murat_unfold(X,Y,Z,mV) +function modv = Murat_unfold(X,Y,Z,mV) % function modv = Murat_unfold(X,Y,Z,mV) % % ACCEPTS matrices created by meshgrid and unfolds them in MuRAT format @@ -12,22 +12,21 @@ % Output parameters: % modv: field in Murat format -ly =size(X,1); -lx =size(X,2); -lz =size(X,3); -modv=zeros(lx*ly*lz,4); +ly = size(X,1); +lx = size(X,2); +lz = size(X,3); +modv = zeros(lx*ly*lz,4); + if (nargin==4) - index = 0; + index = 0; for i=1:lx for j=1:ly for k=1:lz - index = index+1; - modv(index,1:4)... - = [X(j,i,k) Y(j,i,k) Z(j,i,k) mV(j,i,k)]; + index = index+1; + modv(index,1:4) = [X(j,i,k) Y(j,i,k) Z(j,i,k) mV(j,i,k)]; end end end end -% Line to get it in Murat format; end diff --git a/bin/Murat_unfoldXYZ.m b/bin/Murat_unfoldXYZ.m index e995980..796bbeb 100644 --- a/bin/Murat_unfoldXYZ.m +++ b/bin/Murat_unfoldXYZ.m @@ -1,5 +1,5 @@ -function r = Murat_unfoldXYZ(x,y,z) -% function r = Murat_unfold(x,y,z) +function r = Murat_unfoldXYZ(x,y,z) +% function r = Murat_unfold(x,y,z) % % ACCEPTS vertical vectors and unfolds them in standard format % @@ -11,31 +11,24 @@ % Output parameters: % r: 3D field in Murat format -lx = length(x); -ly = length(y); -lz = length(z); -lxyz = lx*ly*lz; +x = x(:); +y = y(:); +z = z(:); -r = zeros(lxyz,3); +lx = numel(x); +ly = numel(y); +lz = numel(z); -index = 1; -for i = 1:lx - index1 = index+ly*lz-1; - gx = repmat(x(i),ly*lz,1); - r(index:index1,1) = gx; - index = index1+1; -end +% x: repeat each x for ly*lz entries +xcol = repelem(x, ly * lz); -index = 1; -ry = zeros(ly*lz,1); -for i = 1:ly - index1 = index+lz-1; - gy = repmat(y(i),lz,1); - ry(index:index1,1) = gy; - index = index1+1; -end +% y: repeat each y for lz entries, then tile for lx blocks +yblock = repelem(y, lz); +ycol = repmat(yblock, lx, 1); -r(:,2) = repmat(ry,lx,1); -r(:,3) = repmat(z,lx*ly,1); -end - \ No newline at end of file +% z: tile z for each x-y pair +zcol = repmat(z, lx * ly, 1); + +r = [xcol, ycol, zcol]; + +end \ No newline at end of file diff --git a/bin/Murat_veldicity.m b/bin/Murat_veldicity.m index 7fd1936..dacb261 100644 --- a/bin/Murat_veldicity.m +++ b/bin/Murat_veldicity.m @@ -1,5 +1,5 @@ -function [vx,vy,vz] = Murat_veldicity(xx,yy,zz,gridD,pvel) -% function [vx,vy,vz] = Murat_veldicity(xx,yy,zz,gridD,pvel) +function [vx,vy,vz] = Murat_veldicity(xx,yy,zz,gridD,pvel) +% function [vx,vy,vz] = Murat_veldicity(xx,yy,zz,gridD,pvel) % % CALCULATES the gradient of the velocity in x, y, and z directions with linear interpolation % @@ -15,47 +15,59 @@ % vy: dv/dx % vz: dv/dx -xGrid = gridD.x; -yGrid = gridD.y; -zGrid = gridD.z; +xg = gridD.x; +yg = gridD.y; +zg = gridD.z; -[ip,jp,kp,flag] = Murat_cornering(xx,yy,zz,gridD); +[ip,jp,kp,flag] = Murat_cornering(xx,yy,zz,gridD); if flag>0 - vx = 0; - vy = 0; - vz = 0; + vx = 0; vy = 0; vz = 0; return end -ip1 = ip+1; -jp1 = jp+1; -kp1 = kp+1; -xd = xGrid(ip1) - xGrid(ip); -yd = yGrid(jp1) - yGrid(jp); -zd = zGrid(kp1) - zGrid(kp); - -xf = (xx - xGrid(ip))/xd; -yf = (yy - yGrid(jp))/yd; -zf = (zz - zGrid(kp))/zd; - -xf1 = 1 - xf; -yf1 = 1 - yf; -zf1 = 1 - zf; - -% calculate derivatives of velocity -vx =(yf1*zf1*(pvel(jp1,ip,kp)-pvel(jp,ip,kp))+... - yf*zf1*(pvel(jp1,ip1,kp)-pvel(jp,ip1,kp))+ ... - yf1*zf*(pvel(jp1,ip,kp1)-pvel(jp,ip,kp1))+ ... - yf*zf*(pvel(jp1,ip1,kp1)-pvel(jp,ip1,kp1)))/xd; - -vy =(xf1*zf1*(pvel(jp,ip1,kp)-pvel(jp,ip,kp))+... - xf*zf1*(pvel(jp1,ip1,kp)-pvel(jp1,ip,kp))+ ... - xf1*zf*(pvel(jp,ip1,kp1)-pvel(jp,ip,kp1))+ ... - xf*zf*(pvel(jp1,ip1,kp1)-pvel(jp1,ip,kp1)))/yd; - -vz =(xf1*yf1*(pvel(jp,ip,kp1)-pvel(jp,ip,kp))+... - xf*yf1*(pvel(jp1,ip,kp1)-pvel(jp1,ip,kp))+ ... - xf1*yf*(pvel(jp,ip1,kp1)-pvel(jp,ip1,kp))+ ... - xf*yf*(pvel(jp1,ip1,kp1)-pvel(jp1,ip1,kp)))/zd; +ip1 = ip+1; +jp1 = jp+1; +kp1 = kp+1; + +xd = xg(ip1) - xg(ip); +yd = yg(jp1) - yg(jp); +zd = zg(kp1) - zg(kp); + +xf = (xx - xg(ip))/xd; +yf = (yy - yg(jp))/yd; +zf = (zz - zg(kp))/zd; + +xf1 = 1 - xf; +yf1 = 1 - yf; +zf1 = 1 - zf; + +% corner values (consistent with original pvel(jp,ip,kp) ordering) +v000 = pvel(jp, ip, kp); +v100 = pvel(jp, ip1, kp); +v010 = pvel(jp1, ip, kp); +v110 = pvel(jp1, ip1, kp); +v001 = pvel(jp, ip, kp1); +v101 = pvel(jp, ip1, kp1); +v011 = pvel(jp1, ip, kp1); +v111 = pvel(jp1, ip1, kp1); + +% dv/dx: differences along x between corresponding corners, weighted by y/z +vx = (yf1*zf1*(v100 - v000) + ... % lower z, lower y + yf*zf1 *(v110 - v010) + ... % lower z, upper y + yf1*zf *(v101 - v001) + ... % upper z, lower y + yf*zf *(v111 - v011)) / xd; % upper z, upper y + +% dv/dy: differences along y between corresponding corners, weighted by x/z +vy = (xf1*zf1*(v010 - v000) + ... % lower z, lower x + xf*zf1 *(v110 - v100) + ... % lower z, upper x + xf1*zf *(v011 - v001) + ... % upper z, lower x + xf*zf *(v111 - v101)) / yd; % upper z, upper x + +% dv/dz: differences along z between corresponding corners, weighted by x/y +vz = (xf1*yf1*(v001 - v000) + ... % lower y, lower x + xf*yf1 *(v101 - v100) + ... % lower y, upper x + xf1*yf *(v011 - v010) + ... % upper y, lower x + xf*yf *(v111 - v110)) / zd; % upper y, upper x + end \ No newline at end of file diff --git a/bin/Murat_velocity.m b/bin/Murat_velocity.m index 3235d70..58107d8 100644 --- a/bin/Murat_velocity.m +++ b/bin/Murat_velocity.m @@ -1,8 +1,7 @@ % FUNCTION Murat_velocity: It finds the velocity at the point (xx,yy,zz) by % linear interpolation. -function v = Murat_velocity(xx,yy,zz,gridD,pvel) - +function v = Murat_velocity(xx,yy,zz,gridD,pvel) % % CALCULATES the velocity at xx, yy, and zz by linear interpolation % @@ -16,40 +15,46 @@ % Output parameters: % v: velocity -xGrid = gridD.x; -yGrid = gridD.y; -zGrid = gridD.z; +xGrid = gridD.x; +yGrid = gridD.y; +zGrid = gridD.z; [ip,jp,kp,flag] = Murat_cornering(xx,yy,zz,gridD); if flag>0 - v = pvel(jp,ip,kp); + v = pvel(jp,ip,kp); return end -ip1 = ip+1; -jp1 = jp+1; -kp1 = kp+1; - -xd = xGrid(ip1) - xGrid(ip); -yd = yGrid(jp1) - yGrid(jp); -zd = zGrid(kp1) - zGrid(kp); - -xf = (xx - xGrid(ip))/xd; -yf = (yy - yGrid(jp))/yd; -zf = (zz - zGrid(kp))/zd; - -v1 = pvel(jp,ip,kp) + (pvel(jp1,ip,kp) -pvel(jp,ip,kp))*xf; - -v2 = pvel(jp,ip1,kp)+(pvel(jp1,ip1,kp) -pvel(jp,ip1,kp))*xf; - -v3 = v1 + (v2 - v1)*yf; - -v4 = pvel(jp,ip,kp1)+(pvel(jp1,ip,kp1) -pvel(jp,ip,kp1))*xf; - -v5 = pvel(jp,ip1,kp1)+... - (pvel(jp1,ip1,kp1) - pvel(jp,ip1,kp1))*xf; - -v6 = v4 + (v5 - v4)*yf; - -v = v3 + (v6 - v3)*zf; +ip1 = ip+1; +jp1 = jp+1; +kp1 = kp+1; + +xd = xGrid(ip1) - xGrid(ip); +yd = yGrid(jp1) - yGrid(jp); +zd = zGrid(kp1) - zGrid(kp); + +xf = (xx - xGrid(ip))/xd; +yf = (yy - yGrid(jp))/yd; +zf = (zz - zGrid(kp))/zd; + +% corner values (consistent indexing: pvel(j,y,x) as original) +v000 = pvel(jp, ip, kp); +v100 = pvel(jp, ip1, kp); +v010 = pvel(jp1, ip, kp); +v110 = pvel(jp1, ip1, kp); +v001 = pvel(jp, ip, kp1); +v101 = pvel(jp, ip1, kp1); +v011 = pvel(jp1, ip, kp1); +v111 = pvel(jp1, ip1, kp1); + +% trilinear interpolation +c00 = v000*(1 - xf) + v100*xf; +c10 = v010*(1 - xf) + v110*xf; +c01 = v001*(1 - xf) + v101*xf; +c11 = v011*(1 - xf) + v111*xf; + +c0 = c00*(1 - yf) + c10*yf; +c1 = c01*(1 - yf) + c11*yf; + +v = c0*(1 - zf) + c1*zf; end \ No newline at end of file diff --git a/bin/Murat_weighting.m b/bin/Murat_weighting.m deleted file mode 100644 index 4c3e52b..0000000 --- a/bin/Murat_weighting.m +++ /dev/null @@ -1,33 +0,0 @@ -function Wc = Murat_weighting(RZZ_k,QcM) -% -% Input parameters: -% RZZ: uncertainty on Qc -% QcM: Linearized or Non Linear measurements. -% Output parameters: -% Wc: weighting matrix - -if isequal(QcM,'Linearized') - - W1 = RZZ_k<0.3; - W2 = RZZ_k<0.5; - W3 = RZZ_k<0.7; - W4 = W3+W2+W1; - Wc = diag(1./(W4+1)); -elseif isequal(QcM,'NonLinear') - probDist = fitdist(RZZ_k,'LogNormal'); - confInterval = paramci(probDist); - - sortRZZ_k = sort(RZZ_k); - pdfValue =... - pdf(probDist,sortRZZ_k)/max(pdf(probDist,sortRZZ_k)); - - W1 = pdfValue<0.3; - W2 = pdfValue<0.5; - W3 = pdfValue<0.7; - W4 = RZZ_kconfInterval(2); - W4 = W4+W3+W2+W1; - Wc = diag(1./(W4+1)); - -end - -end \ No newline at end of file diff --git a/bin/SetFDefaults.m b/bin/SetFDefaults.m index 55eb5ab..f639cb3 100644 --- a/bin/SetFDefaults.m +++ b/bin/SetFDefaults.m @@ -1,10 +1,23 @@ function SetFDefaults() -% DEFAULT settings for MuRAt figures -ax = gca; -ax.GridColor = [0 0 0]; -ax.GridLineStyle = '--'; -ax.GridAlpha = 0.3; -ax.LineWidth = 1.5; -ax.FontSize = 12; -set(ax,'FontSize',20); -grid on \ No newline at end of file +% DEFAULT settings for MuRAT figures +h = gca; +if isa(h,'matlab.graphics.axis.Axes') + safeSet(h, 'GridColor', [0 0 0]); + safeSet(h, 'GridLineStyle', '--'); + safeSet(h, 'GridAlpha', 0.3); + safeSet(h, 'LineWidth', 1.5); + safeSet(h, 'FontSize', 12); + safeSet(h, 'XGrid', 'on'); + safeSet(h, 'YGrid', 'on'); +end +grid on + +% Helper: set property only when supported and catch set failures +function safeSet(h, prop, val) +if any(strcmp(prop, properties(h))) + try + set(h, prop, val); + catch + % ignore failures silently + end +end \ No newline at end of file diff --git a/bin/myfig.m b/bin/myfig.m new file mode 100644 index 0000000..e63ef92 --- /dev/null +++ b/bin/myfig.m @@ -0,0 +1,28 @@ +function f = myfig(figName, varargin) +% myfig Create figure with preset defaults and optional name. +% f = myfig() -> creates figure with defaults, no title. +% f = myfig('My Title') -> creates figure with Name = 'My Title'. +% f = myfig('', 'Visible','on','Position',[0 0 800 600]) +% -> override defaults with Name-Value pairs. +% +% Returns the Figure object. + +if nargin < 1, figName = ''; end + +% default properties +defaultPos = [20 400 1200 1000]; +defaults = { ... + 'NumberTitle', 'off', ... + 'Position', defaultPos, ... + 'Visible', 'off' ... + }; + +% include Name if provided +if ~isempty(figName) + defaults = [{'Name', figName}, defaults]; +end + +% call figure with defaults first, then user overrides (varargin last wins) +f = figure(defaults{:}, varargin{:}); + +end \ No newline at end of file diff --git a/bin/saveFigureAsImage.m b/bin/saveFigureAsImage.m new file mode 100644 index 0000000..56a878e --- /dev/null +++ b/bin/saveFigureAsImage.m @@ -0,0 +1,100 @@ +function outFile = saveFigureAsImage(pathFolder, fig) +% saveFigureAsImage Capture figure and write to PNG. +% outFile = saveFigureAsImage(pathFolder) captures gcf and writes to +% pathFolder.png (or pathFolder if it already has .png). +% outFile = saveFigureAsImage(pathFolder, fig) captures specified figure +% handle. +% +% Returns full path of written file. + +arguments + pathFolder (1,:) char + fig = gcf +end + +% Force white figure background +fig.Color = [1 1 1]; + +% Find all axes (including tiled, polar, etc.) +ax = findall(fig, 'Type', 'axes'); +for k = 1:numel(ax) + % Axes background white + ax(k).Color = [1 1 1]; + % Axes lines, tick labels, and title/label text to black + ax(k).XColor = [0 0 0]; + ax(k).YColor = [0 0 0]; + if isprop(ax(k),'ZColor') % cartesian 3D axes + ax(k).ZColor = [0 0 0]; + end + % Set label and title colors (covers xlabel/ylabel/title objects) + try + ax(k).Title.Color = [0 0 0]; + catch + end + try + ax(k).XLabel.Color = [0 0 0]; + ax(k).YLabel.Color = [0 0 0]; + if isprop(ax(k),'ZLabel') + ax(k).ZLabel.Color = [0 0 0]; + end + catch + end + % Tick label interpreter may be preserved; ensure color for text objects + tickText = findall(ax(k), 'Type', 'text'); + for t = 1:numel(tickText) + tickText(t).Color = [0 0 0]; + end +end + +% Configure legends +lg = findall(fig, 'Type', 'legend'); +for L = 1:numel(lg) + lg(L).TextColor = [0 0 0]; % legend text black + lg(L).Color = [1 1 1]; % legend background white + lg(L).EdgeColor = [0 0 0]; % legend border black + % If legend contains title + try + lg(L).Title.Color = [0 0 0]; + catch + end +end + +drawnow; % ensure updates take effect + +% Resolve figure handle +if isempty(fig) || ~ishandle(fig) + fig = gcf; +end + +% Ensure filename ends with .png or treat as folder +[pth, name, ext] = fileparts(pathFolder); +if isempty(ext) + % If pathFolder is an existing folder, use default name + if isfolder(pathFolder) + pth = pathFolder; + name = 'figure'; + end + ext = '.png'; +end + +outFile = fullfile(pth, [name, ext]); + +% Create parent folder if needed +[outDir, ~, ~] = fileparts(outFile); +if ~isempty(outDir) && ~isfolder(outDir) + mkdir(outDir); +end + +% Use a fast exporter +origRenderer = fig.Renderer; +fig.Renderer = 'painter'; % good for raster PNGs +drawnow; % ensure content is up-to-date + +% Use exportgraphics which is typically faster +% You can set resolution with 'Resolution',300 if needed. +exportgraphics(fig, outFile, 'BackgroundColor', 'white', 'Resolution', 72); + +% restore renderer +fig.Renderer = origRenderer; + +end \ No newline at end of file diff --git a/bin/tryIndex.m b/bin/tryIndex.m new file mode 100644 index 0000000..3dc8375 --- /dev/null +++ b/bin/tryIndex.m @@ -0,0 +1,29 @@ +function val = tryIndex(mat, iLon1, iLat1, iLat2, iLon2) + % If mat is 2D and size matches [length(lons), length(lats)] assume (lon,lat) + sz = size(mat); + val = NaN; + if numel(sz) == 2 + if sz(1) >= iLon1 && sz(2) >= iLat1 + try val = mat(iLon1, iLat1); return; end + end + if sz(1) >= iLat2 && sz(2) >= iLon2 + try val = mat(iLat2, iLon2); return; end + end + % fallback: linear index if sizes match total + if numel(mat) >= max(iLon1,iLat1) + try val = mat(iLon1); return; end + end + elseif numel(sz) == 3 + % if there's an extra singleton or layer dim, pick the first page + % try common permutations + try + if sz(1) >= iLon1 && sz(2) >= iLat1 + val = mat(iLon1, iLat1, 1); return; + elseif sz(1) >= iLat2 && sz(2) >= iLon2 + val = mat(iLat2, iLon2, 1); return; + end + catch + end + end + % if all fails, return NaN +end \ No newline at end of file diff --git a/velocity_models/LITHO1.0.nc b/velocity_models/LITHO1.0.nc new file mode 100644 index 0000000..2d333a3 Binary files /dev/null and b/velocity_models/LITHO1.0.nc differ