Monitoring of Mining-Induced Surface Subsidence Based on Multi-Source Data Fusion

A method for extracting mining-induced surface subsidence based on multi-source data fusion is proposed by integrating the advantages of InSAR, PIM, and UAV technologies. This approach first employs InSAR to obtain time-series cumulative subsidence basins and constructs constraint conditions to distinguish between high-gradient deformation zones, enabling the differentiation of subsidence edges and centers. In the subsidence edge areas, InSAR monitoring results are retained, while in the central zones with large-gradient subsidence, a weighted fusion of PIM and UAV data is conducted to establish a subsidence basin model for the mining working face. Subsequently, spatial interpolation is applied to derive continuous surface subsidence information in a geographic coordinate system, ultimately generating a complete surface subsidence map of the mining area. The method is validated using Sentinel-1A satellite imagery, UAV data, and leveling measurements from the 120101 working face of the Yangchangwan Coal Mine in the Ningdong mining area. The results demonstrate that this method effectively compensates for the limitations of using InSAR, PIM, or UAV data individually, enhances monitoring capability in high-gradient subsidence areas, and enables the acquisition of more accurate and spatially continuous surface subsidence information.