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).
  
• Spatial Alignment: Reprojected all layers to UTM Zone 43N.
  

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

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.

---

📊 Results

1. Predictive Maps

• Hotspots: 12 new target zones identified.

2. 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(...)

---

📦 Deliverables

1. Code: GitHub Repo (Notebooks + scripts).
2. Reports:
   • Technical Report (PDF).
   • Presentation Slides.
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

• GSI Data Portal
• ASTER Spectral Indices Guide

---

🌟 Hackathon Submission by Team GeoSurfers
Powered by Python and Open Geoscience Data