Basin-Wide Monitoring and PIM Parameter Inversion of Mining Subsidence Using UAV-LiDAR

Abstract

Accurate, comprehensive, and spatially continuous monitoring of mining-induced surface subsidence is essential for geohazard prevention, ecological restoration, and safe mining. Conventional approaches, however, are limited by the sparse spatial distribution of GNSS observations, the difficulty of InSAR in resolving large and rapidly evolving deformation, and the reliance of probability integral method (PIM) calibration on sparse observations. Here, we evaluate an integrated UAV-LiDAR–PIM workflow for basin-wide characterization of mining-induced subsidence and improved spatial constraint of PIM parameters. We use the 3206 working face in an Ordos mining area as a case study. DEM differencing of multi-temporal UAV-LiDAR observations characterizes the spatial distribution of the subsidence basin. Uniform sampling within the affected zone then provides a high-density dataset for PIM calibration, which we compare with conventional profile-based monitoring. The two UAV-LiDAR DEM epochs yielded vertical quality-control RMSEs of 0.052 and 0.050 m, respectively, with a mean of 0.051 m. These results support their use for basin-scale deformation analysis. The inverted angular parameters indicate a larger mining influence extent along the dip direction than along the strike. Surface movement followed initial, active, and declining stages over 423 days, with a maximum subsidence rate of 87.39 mm d−1 during the active stage. The integrated workflow reduces the spatial-sampling limitations of profile-based monitoring and adds two-dimensional constraints on basin geometry and directional variation. Combining basin-wide UAV-LiDAR observations with conventional profiles may improve site-specific PIM calibration and support mining-hazard assessment and ecological-restoration planning.