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AI-Driven Mineral Targeting in Karnataka-Andhra Pradesh

Leveraging Geoscience Data and Machine Learning to Discover Concealed Mineral Deposits

📌 Table of Contents

  1. Project Overview
  2. Key Features
  3. Directory Structure
  4. Setup Instructions
  5. Methodology
  6. Results
  7. Deliverables
  8. Future Work

🌍 Project Overview

Objective: Identify concealed mineral deposits (Au, Cu, PGEs) in a 39,000 sq. km area using AI/ML.
Datasets: Geological (GSI 25K/50K), Geochemical (NGCM), Aeromagnetic, ASTER Remote Sensing.
Tech Stack: Python, Scikit-learn, XGBoost, GeoPandas, Rasterio, SHAP.

✨ Key Features

  • Automated Data Pipeline: Integration of multi-source geoscience data.
  • Feature Engineering: Geochemical ratios, fault proximity, spectral indices.
  • Explainable AI: SHAP values for model transparency.
  • 3D-Ready Outputs: Predictive maps compatible with QGIS/ArcGIS.

📂 Directory Structure

project-root/
├── datasets/               # Raw geoscience data from GSI
├── final_datasets/         # Processed CSVs/shapefiles
├── images/                 # Visualizations (EDA, results)
├── notebooks/              # Jupyter notebooks (EDA → Modeling)
└── venv/                   # Conda environment

🛠 Setup Instructions

Option 1: Conda (Recommended)

conda create -p ./venv python=3.12 -y
conda activate ./venv
pip install -r requirements.txt

Option 2: Virtualenv (venv)

python -m venv venv
source venv/bin/activate  # Linux/Mac | venv\Scripts\activate on Windows
pip install -r requirements.txt

Libraries Installed

geopandas, rasterio, scikit-learn, xgboost, shap, matplotlib, seaborn

🔍 Methodology

1. Data Preprocessing

  • Geochemical Data: Log-transformed skewed elements (Cu, Au).
    Cu Distribution
  • Spatial Alignment: Reprojected all layers to UTM Zone 43N.
    Lithology Map

2. Feature Engineering

Feature Type Example Significance
Geochemical Ratios Cu/Zn, Ni/Cr Indicator of mineralization
Structural Proximity Distance to Faults Controls fluid pathways
Spectral Indices Clay/Silica Ratio Hydrothermal alteration

Clay/Silica Ratio

3. Model Building

  • Algorithms: Random Forest (AUC: 0.89) vs. XGBoost (AUC: 0.91).
  • Validation: 78% of high-probability points matched GSI’s known blocks.

Confusion Matrix

📊 Results

1. Predictive Maps

Prospectivity Map

  • Hotspots: 12 new target zones identified.

2. Feature Importance

XGBoost Feature Importance

  • Top Predictors: Cu_ppm, Magnetic_Anomaly, Clay_Index.

3. 3D Depth Modeling (Conceptual)

Gravity inversion for depth estimates:

# Pseudocode: SimPEG inversion
survey = gravity.survey.Survey(...)
model = gravity.Inversion.run(...)

Magnetic Gradient

📦 Deliverables

  1. Code: GitHub Repo (Notebooks + scripts).
  2. Reports:
  3. GIS Outputs:
    • final_prospectivity_map.shp (QGIS/ArcGIS).
    • mineral_probability_map.csv.

🚀 Future Work

  • Borehole Integration: Calibrate depth models with drill data.
  • Web App: Deploy with Streamlit for interactive exploration.
  • Multi-Model Ensemble: Improve robustness with hybrid ML approaches.

🔗 References

🌟 Hackathon Submission by Team GeoSurfers
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