Improved Method for Unstable Slope Identification in Coal-Mining Mountainous Areas Combining InSAR and Clustering Techniques

Surface deformation triggered by coal extraction activities, together with the consequent development of unstable slopes within rugged mountainous landscapes, constitutes a critical focus for geological risk assessment and mitigation strategies. Conventional SBAS-InSAR processing pipelines suffer from inadequate tropospheric phase mitigation in topographically complex environments, while existing clustering-based recognition approaches fail to incorporate sufficient geophysical constraints. To overcome these deficiencies, the present investigation introduces a refined methodology that synergizes InSAR measurements with an enhanced clustering scheme for the automated screening of potentially unstable slope units. First, a two-stage coupled atmospheric correction framework is constructed within the SBAS-InSAR processing chain, comprising spatially varying stratified atmosphere estimation based on geographically weighted robust regression (GWRR-M) and turbulent atmosphere compensation based on structure-guided deformation-preserving interpolation (SGDPI); both stages require no external meteorological data and effectively protect deformation signals from overcorrection. Second, a spatiotemporally constrained density peak clustering algorithm (STC-DPC) is developed, which constructs a multi-dimensional feature space integrating spatial location, deformation rate, temporal evolution characteristics, and topographic-geological background, and introduces a spatiotemporally constrained distance metric together with an Unstable Slope Index (USI) to achieve automatic identification and quantitative discrimination of unstable slopes. The proposed method was evaluated using 120 ascending-track Sentinel-1A SAR images acquired from 2019 to 2023 over the coal-mining mountainous areas of Mentougou and Fangshan districts in western Beijing, China. The results show that the improved atmospheric correction reduces the phase standard deviation of a representative interferogram from 1.6 rad to 0.6 rad, with an average reduction of 42.3% across all interferograms. A total of 187 unstable slopes were identified by the STC-DPC algorithm, mainly distributed in abandoned mining areas and steep terrain with gradients of 10–35°, with a mean deformation rate of −25.3 mm/a; field investigations at representative sites confirmed significant deformation evidence (e.g., tension cracks and bulging), providing qualitative support for the identification results. Compared with the identification results obtained without atmospheric correction (79 unstable slopes), the improved method improves the detectability of weak deformation signals in areas with strong topographic relief and diverse deformation patterns. This study provides a practical technical pathway for the early screening and monitoring of geological hazards in coal-mining mountainous areas and holds great significance for mine ecological restoration and regional disaster prevention and mitigation.