Application of Small Baseline Set Time-Series InSAR Technique in Landslide Disaster Monitoring in Southern Hilly Mining Area

Abstract

Featured Application : This study presents an innovative multi-source data fusion method for the quantitative identification of landslide hazards in mountainous open-pit mining areas, offering new insights and technical approaches for the research and prevention of such disasters. The method provides a scientific basis for identifying and assessing landslide risks in mountainous open-pit mining areas, demonstrating potential for application in similar regions. The findings contribute to the development of more effective disaster prevention and mitigation measures, ensuring mining site safety and promoting regional sustainable development. Mountainous open-pit mines are highly susceptible to landslides, yet quantitative risk assessment remains a challenge. This study aims to develop and validate a quantitative landslide risk assessment model by integrating multi-source data to enhance hazard identification in these complex environments. Taking the Dexing Copper Mine as a case study, we used Small Baseline Subset InSAR (SBAS-InSAR) to derive surface deformation rates. This deformation data was integrated with geological and topographical factors within a Geographic Information System (GIS), using an information value model combined with weighting from the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to generate a comprehensive landslide risk map. The results show that 3860 potential landslide points were identified, with deformation rates ranging from −338.74 to 80.61 mm/a. High and very high-risk zones were primarily concentrated around the Fujiawu and Zhujiawu dump sites, and the model’s performance was validated with a high degree of accuracy, achieving an Area Under the Curve (AUC) value of 0.871. This study demonstrates that the integration of multi-source data provides a robust and effective approach for quantitative landslide risk assessment in mountainous mining areas. The proposed framework can serve as a valuable tool for targeted disaster prevention and management.