Geospatial risk modeling of subsidence-related hazards in mining-affected terrains using remote sensing, geophysics, and VR-based visualization

Subsidence hazards in post-mining and tectonically complex terrains increasingly threaten infrastructure, human safety, and land-use stability, especially in data-scarce regions without in-situ monitoring. This study develops a geospatial risk modeling framework that integrates gravity (Bouguer) anomalies, magnetic gradients, satellite-derived slope, Normalized Difference Vegetation Index (NDVI), and dry surface extent into a composite Physical Risk Index (PRI) for terrain vulnerability classification. We apply the method in Brazil’s Iron Quadrangle, where structurally unstable zones frequently overlap with expanding urban areas and legacy mining sites. The resulting risk surface supports early detection, spatial prioritization, and mitigation planning in hazard-prone landscapes. A key contribution is a reproducible pipeline that fuses multisource remote sensing and geophysical data into a standardized risk model, enabling instability detection in data-limited contexts. We embed the output in an immersive virtual reality (VR) interface to enhance spatial interpretation and support decision-making by non-specialists. Although applied in a South American mining region, the framework is transferable to other areas facing subsidence, collapse, or structural hazards. It provides an actionable tool for civil protection agencies, urban planners, and infrastructure managers at the intersection of geomatics, hazard assessment, and spatial risk governance.