Geotechnical Risk Prediction System

A machine learning system for predicting geotechnical hazards — including rockburst and slope instability events — in underground and open-pit mining operations.

The challenge

Underground mining operations face inherent geotechnical risks. Rock mass under stress can fail suddenly, creating hazards for equipment and personnel. Traditional assessment relies on manual geological surveys and rule-of-thumb thresholds applied to seismic monitoring data. These approaches miss complex spatial and temporal patterns that precede major events.

Approach

The system transforms raw seismic monitoring data into actionable risk predictions through a multi-stage pipeline:

Feature engineering

Raw seismic event catalogs are transformed into meaningful indicators:

• Energy indices: cumulative and windowed seismic energy release patterns
• Spatial features: event clustering, migration patterns, proximity to geological structures
• Temporal patterns: event rate changes, quiescence periods, frequency-magnitude distributions
• Block model integration: geological and geomechanical properties from 3D mine models

Classification

Ensemble ML models (XGBoost with SHAP explainability) classify spatial blocks into risk levels, providing both predictions and interpretable feature importance for each assessment. Geologists can understand why a zone is flagged, not just that it is.

Operational integration

Risk predictions feed into operational planning systems, informing decisions about:

• Access restrictions for high-risk zones
• Blasting sequence optimization to manage induced seismicity
• Support design recommendations based on predicted stress conditions

Technology stack

• Pipeline: Kedro 1.0 with Databricks Asset Bundles for deployment
• ML: XGBoost with SHAP explanations, scikit-learn preprocessing
• Data: PySpark + Delta Lake with Unity Catalog governance
• Deployment: Multi-environment (dev/preprod/prod) with CI/CD
• Output: JSON risk assessments consumed by operational planning systems

Note: This description reflects the general type and architecture of systems I have built. Specific client details and operational data are omitted.