Typed objects variogram

code-samples/geoscience-objects/README.md

@@ -6,6 +6,8 @@ The `publish-*` directories contain Jupyter notebooks with sample code for uploa
 
 **New users should start with `simplified-object-interactions/`** - This example demonstrates the recommended approach for most users and geologists using the typed objects API (`PointSet`, `Regular3DGrid`, etc.) with the `evo.widgets` extension for rich HTML display. It provides a simpler, more intuitive way to upload and download geoscience objects.
 
+**For geostatistical workflows, see `running-kriging-compute/`** - This example demonstrates a complete workflow including creating pointsets, variogram models, and visualizing them together with Plotly. It also includes WIP sections for kriging estimation using Evo Compute.
+
 The `publish-*` examples use the lower-level `evo-schemas` approach, which offers more control but requires more boilerplate code.
 
 ## Requirements

code-samples/geoscience-objects/running-kriging-compute/README.md

@@ -0,0 +1,55 @@
+# Running Kriging Compute
+
+This example demonstrates a complete geostatistical workflow using the Evo Python SDK:
+
+1. **Load downhole assay data** as a PointSet
+2. **Define a variogram model** for spatial correlation
+3. **Visualize** the pointset and variogram together with Plotly
+4. **Run kriging estimation** using Evo Compute (WIP)
+
+## Overview
+
+The workflow uses the WP_assay.csv dataset containing copper (CU_pct) and gold (AU_gpt) assay values from 55 downhole. We'll:
+
+- Create a `PointSet` from the CSV data
+- Define a nested spherical `Variogram` model for copper grades
+- Extract and scale `Ellipsoid` objects for search neighborhoods
+- Visualize the data, variogram curves, and anisotropy ellipsoids with Plotly
+- Set up kriging estimation parameters (WIP)
+
+## Dataset Characteristics
+
+- **8,332 sample points** from 55 downhole
+- **Spatial extent**: ~936m (X) × ~1,416m (Y) × ~855m (Z)
+- **Coordinate system**: EPSG:32650 (UTM Zone 50N)
+- **Target attribute**: CU_pct (copper percentage)
+  - Mean: 0.95%, Variance: 0.84
+
+## Variogram Model
+
+The variogram uses two nested spherical structures aligned with the dominant orientation of the downhole data:
+- **Nugget**: 0.08 (~10% nugget effect)
+- **Short-range structure**: Contribution 0.25, ranges 80m × 60m × 40m
+- **Long-range structure**: Contribution 0.51, ranges 250m × 180m × 100m
+- **Anisotropy**: Dip 70°, Azimuth 15° (NNE strike direction)
+
+## WIP: Kriging Compute
+
+The notebook includes work-in-progress sections demonstrating:
+- Creating a target `BlockModel` for estimation
+- Configuring `KrigingParameters` with search neighborhoods
+- Running kriging tasks with `evo.compute`
+- Running multiple scenarios in parallel for sensitivity analysis
+
+## Requirements
+
+- Python 3.10+
+- Seequent account with Evo entitlement
+- Evo application credentials (client ID and redirect URL)
+
+## Quick Start
+
+1. Open `running-kriging-compute.ipynb` in Jupyter
+2. Update the `client_id` and `redirect_url` with your Evo app credentials
+3. Run the cells to create objects and visualize the geostatistical model
+