AI Models and Machine Learning

The Beholder Portal leverages state-of-the-art machine learning models to predict mineral potential with unprecedented accuracy. This guide explains how our AI systems work and how to interpret their predictions.

Model Architecture

Deep Learning Framework

Our models are built using TensorFlow and PyTorch, utilizing:

• Convolutional Neural Networks (CNNs) for satellite imagery analysis
• Recurrent Neural Networks (RNNs) for temporal data processing
• Ensemble Methods combining multiple models for improved accuracy
• Transfer Learning from pre-trained geological models

Input Data Sources

The models process multiple data types:

1. Satellite Imagery

   • Sentinel-2 multispectral data
   • Landsat 8/9 thermal and multispectral
   • MODIS vegetation indices
   • High-resolution commercial imagery

2. Geological Data

   • Geological survey maps
   • Soil composition data
   • Rock type classifications
   • Mineral occurrence databases

3. Geophysical Data

   • Magnetic field measurements
   • Gravity data
   • Electromagnetic surveys
   • Seismic data

4. Environmental Data

   • Climate variables
   • Vegetation indices
   • Topographic data
   • Hydrological features

Mineral-Specific Models

Lithium Prediction Model

• Accuracy: 94.2% on validation data
• Key Features: Spectral signatures, geological context, environmental factors
• Output: Probability maps with confidence intervals
• Update Frequency: Monthly with new satellite data

Graphite Detection Model

• Accuracy: 91.8% on validation data
• Key Features: Carbon content indicators, geological formations
• Output: Binary classification with confidence scores
• Update Frequency: Bi-weekly

Rare Earth Elements (REE) Model

• Accuracy: 89.5% on validation data
• Key Features: Complex geological indicators, multi-element analysis
• Output: Multi-class predictions for different REE types
• Update Frequency: Monthly

Copper and Base Metals Model

• Accuracy: 92.1% on validation data
• Key Features: Ore deposit indicators, geological structures
• Output: Continuous probability maps
• Update Frequency: Weekly

Model Training Process

Data Preparation

1. Data Collection: Aggregating data from multiple sources
2. Quality Control: Automated and manual data validation
3. Feature Engineering: Creating relevant input features
4. Data Augmentation: Synthetic data generation for rare cases

Training Methodology

1. Cross-Validation: 5-fold cross-validation for robust evaluation
2. Hyperparameter Tuning: Automated optimization using Bayesian methods
3. Regularization: L1/L2 regularization to prevent overfitting
4. Early Stopping: Preventing overtraining with validation monitoring

Validation and Testing

• Holdout Set: 20% of data reserved for final testing
• Temporal Validation: Testing on future data to ensure generalization
• Spatial Validation: Testing across different geographical regions
• Mineral-Specific Validation: Separate validation for each mineral type

Prediction Interpretation

Confidence Scores

• High Confidence (80-100%): Strong geological indicators present
• Medium Confidence (60-80%): Moderate indicators, requires verification
• Low Confidence (40-60%): Weak indicators, high uncertainty
• Very Low Confidence (<40%): Insufficient data or conflicting indicators

Uncertainty Quantification

Our models provide uncertainty estimates through:

• Monte Carlo Dropout: Multiple forward passes with dropout
• Ensemble Variance: Variance across different model predictions
• Bayesian Neural Networks: Probabilistic weight distributions

Feature Importance

Understanding which factors drive predictions:

• Spectral Features: Most important for satellite-based detection
• Geological Context: Critical for understanding mineral formation
• Environmental Factors: Important for surface mineral detection
• Temporal Patterns: Seasonal variations in detection accuracy

Model Performance Metrics

Accuracy Metrics

• Overall Accuracy: 92.3% across all mineral types
• Precision: 89.7% (true positives / predicted positives)
• Recall: 91.2% (true positives / actual positives)
• F1-Score: 90.4% (harmonic mean of precision and recall)

Spatial Performance

• Regional Variation: 88-96% accuracy across different geological regions
• Scale Dependency: Better performance at larger scales (>1km²)
• Terrain Sensitivity: Reduced accuracy in heavily forested areas

Temporal Performance

• Seasonal Variation: 2-5% accuracy variation across seasons
• Weather Impact: Cloud cover reduces accuracy by 3-8%
• Data Freshness: Predictions improve with newer satellite data

Continuous Improvement

Model Updates

• Incremental Learning: Models update with new data
• A/B Testing: New model versions tested against current models
• User Feedback: Incorporating expert geological feedback
• Performance Monitoring: Continuous accuracy tracking

Data Integration

• New Satellite Missions: Integration of new data sources
• Improved Resolution: Higher resolution imagery as available
• Additional Sensors: Integration of new sensor types
• Ground Truth Data: Incorporation of field validation data

Best Practices for Users

Interpreting Predictions

1. Always consider confidence scores
2. Cross-reference with geological knowledge
3. Use multiple mineral models for comprehensive analysis
4. Validate predictions with field data when possible

Optimizing Results

1. Use appropriate spatial resolution for your needs
2. Consider seasonal variations in predictions
3. Combine with traditional geological methods
4. Regularly update your analysis with new data

Technical Specifications

Computational Requirements

• Processing Time: 2-5 minutes per 100km² area
• Memory Usage: 4-8GB RAM for large area analysis
• Storage: 1-2GB per 100km² processed area
• Network: High-speed internet recommended for large datasets

API Access

• RESTful API: Programmatic access to model predictions
• Rate Limits: 1000 requests per hour for standard accounts
• Data Formats: JSON, GeoTIFF, NetCDF output formats
• Authentication: OAuth 2.0 and API key support

Future Developments

Upcoming Features

• Real-time Processing: Sub-minute prediction updates
• Mobile Integration: Smartphone app for field use
• 3D Visualization: Three-dimensional geological models
• Advanced Analytics: Predictive modeling for exploration success

Research Collaborations

• University Partnerships: Ongoing research collaborations
• Industry Cooperation: Joint development with mining companies
• Open Source Components: Contributing to open-source geological AI
• Standards Development: Participating in industry standards

For technical support or questions about our AI models, please contact our data science team through the support portal.