Faults, fractures, and folds play a critical role in controlling mineralization and understanding regional structural frameworks.
With GeoSense™, fault detection no longer relies on field-based surveys or single-layer satellite images—it’s now automated using fused radar and elevation data.
Technical Workflow
- Preprocessing SAR and DEM layers: Prepares synthetic aperture radar and digital elevation data for integrated analysis.
- Slope, curvature, and drainage anomaly extraction: Identifies topographic patterns that often align with fault and fracture zones.
- AI-based ridge line and fracture clustering: Applies machine learning to group structural trends across vast terrains.
- Generating fault orientation and length statistics: Quantifies geometry of mapped faults for structural modeling and mineral targeting.
- Exporting to GIS platforms: Outputs shapefiles and layers for seamless overlay with geological and geochemical data.
Case Study: Rift Basin Mapping in Africa
- GeoSense™ was deployed to support structural mapping in a remote African rift basin.
- Using open-source Sentinel-1 and SRTM datasets, the system identified and mapped over 300 individual fault segments.
- This automated approach reduced dependence on field-based validation efforts by over 80 percent.
GeoSense™ streamlines structural interpretation by fusing elevation and radar signals—bringing speed, consistency, and geospatial intelligence to fault mapping.
Key Takeaways
- Automates structural framework detection across large areas using AI and remote sensing fusion.
- Significantly reduces reliance on field teams and manual image interpretation for fault identification.
- Delivers georeferenced fault shapefiles compatible with all major GIS and exploration tools.