Phase 1: Understanding the Problem & Data Exploration
- Understand the Objective Goal: Identify concealed & deep-seated mineral deposits (REE, Ni-PGE, Copper, Diamond, Iron, Manganese, Gold) in Karnataka & Andhra Pradesh.
Key Tasks:
Data Integration: Combine geological, geophysical, geochemical, and remote sensing data.
AI/ML Modeling: Develop models for mineral prediction.
Depth Modeling: Create 3D models of mineralized zones.
Predictive Mapping: Generate exploration target maps.
- Study the Provided Datasets Geological Maps (25K & 50K scale) → Lithology, structural trends.
Geochemical Data (NGCM) → Elemental concentrations (indicators of mineralization).
Aeromagnetic & Gravity Data → Subsurface anomalies (indicate mineral deposits).
ASTER Mineral Maps → Surface mineralogy (clay, iron oxides, etc.).
Lineament Data → Faults & fractures (controls mineralization).
Technical Reports → Past exploration insights.
- Additional Data Sources (If Needed) USGS EarthExplorer (Landsat, ASTER).
Bhuvan Portal (Indian remote sensing data).
OpenGeoscience portals (Global datasets).
Phase 2: Data Preprocessing & Feature Engineering
- Data Cleaning & Preparation Handling Missing Data: Impute or remove incomplete entries.
Normalization/Standardization: Ensure uniform scales (e.g., geochemical ppm values vs. gravity mGal).
Coordinate Alignment: Ensure all spatial datasets use the same projection (e.g., WGS84 UTM Zone 43N).
- Feature Extraction From Geological Maps:
Rock type classifications.
Structural features (folds, faults).
From Geochemical Data:
Elemental ratios (e.g., Cu/Zn, Ni/Cr for PGEs).
Anomaly detection (Z-score, PCA).
From Geophysical Data:
Magnetic & gravity gradients.
Depth estimation (Euler Deconvolution, Inversion Models).
From Remote Sensing (ASTER):
Hydrothermal alteration indices (e.g., Clay, Iron Oxide ratios).
Lineament density.
- Data Integration GIS-Based Overlay Analysis (QGIS/ArcGIS).
3D Geological Modeling (Leapfrog, GemPy).
Phase 3: AI/ML Model Development
- Choosing the Right Approach Task Possible AI/ML Techniques Mineral Prospectivity Mapping Random Forest, SVM, XGBoost Anomaly Detection Isolation Forest, Autoencoders 3D Depth Modeling Neural Networks, Gaussian Processes Multi-Sensor Data Fusion Deep Learning (CNN, U-Net)
- Model Training & Validation Training Data: Use known mineralized zones (from GSI reports) as positive samples.
Validation: Cross-validation, ROC-AUC analysis.
Explainability: SHAP values to interpret feature importance.
- Output Generation Predictive Maps (Probability of mineralization).
3D Models (Leapfrog, Python-based visualization).
Uncertainty Estimation (Confidence intervals).
Phase 4: Documentation & Report Preparation
- Project Report Structure Team Details (Names, Roles).
Resources Used (Python, TensorFlow, QGIS, Cloud GPUs).
Data Sources (List all datasets used).
Methodology (Flowchart of steps).
Results (Maps, 3D models, statistical significance).
Error Analysis (Model limitations).
Future Work (How GSI can refine the approach).
- Code Documentation GitHub Repository (Well-commented Jupyter notebooks).
Dependencies (requirements.txt).
README (How to reproduce results).
Phase 5: Presentation & Submission
- Virtual Presentation Slides Structure:
Problem Statement → Methodology → Results → Conclusion.
Visual Aids:
Heatmaps, 3D renderings, ROC curves.
Demo: Short video/GIF of model predictions.
- Final Submission Checklist ✅ Code (GitHub link).
✅ Report (PDF, max 15 pages).
✅ Predictive Maps & 3D Models (GeoTIFF, OBJ files).
✅ Presentation Slides (PDF/PPT).
Key Tips for Success ✔ Focus on Explainability – Judges need to trust your model. ✔ Leverage Domain Knowledge – Consult geological papers on Karnataka/Andhra mineralization. ✔ Iterative Testing – Try multiple models (start simple, then go complex). ✔ Collaborate – If possible, team up with a geologist.
Timeline Suggestion Week Task Week 1 Data Exploration & Cleaning Week 2 Feature Engineering & Baseline Models Week 3 Advanced Modeling & 3D Visualization Week 4 Report Writing & Presentation Prep Judging Criteria Recap Data Handling (30%) – How well you processed complex datasets.
Innovation (30%) – Novelty in AI/ML application.
Results (30%) – New prospective zones identified.
Presentation (10%) – Clarity & professionalism.