Addressing the critical challenges of insufficient precision in landslide hazard identification and the lack of dynamic stability assessment for high-steep slopes in open-pit mines, this study innovatively proposes an integrated technical framework that deeply fuses time-series Interferometric Synthetic Aperture Radar (InSAR) with machine learning (ML), achieving an intelligent analysis framework that integrates deformation monitoring and stability assessment for open-pit mine landslide hazards. The main contributions include: (1) overcoming the limitations of InSAR technology in low-coherence areas, an improved Small Baseline Subset InSAR (SBAS-InSAR) algorithm was adopted to extract slope deformation, increasing the monitoring point density on complex rock slopes by a factor of 2.18, obtaining high-precision deformation fields, and significantly enhancing the deformation capture capability of high-steep slopes; (2) a new paradigm of dynamic-static multi-factor coupled stability assessment was proposed, which deeply fuses time-series InSAR deformation characteristics with multi-source heterogeneous data, including geological, mining, and environmental factors, employing a dual-model collaborative strategy of Random Forest (RF) and XGBoost achieving an Area Under the Curve (AUC) exceeding 0.88, with the InSAR dynamic factor contributing the highest importance, thereby validating the core role of dynamic monitoring data in stability assessment. The empirical study at a large open-pit mine in northern China demonstrates that high- and very-high-risk zones are precisely localized to specific benches, providing an operational technical support system for mine safety production and offering significant demonstration value for promoting the deep application of InSAR technology in the field of mine disaster early warning.
Zhou et al. (Sat,) studied this question.