Monitoring and analysis of surface subsidence in mining areas of integrated SBAS-InSAR and probability integral method

To address the issue of optimizing the accuracy of surface subsidence data caused by mining activities, SBAS-InSAR technology and probability integration method are combined to obtain refined surface subsidence information in mining areas. The SBAS-InSAR technique is utilized to acquire the cumulative surface subsidence in the mining area. Based on the genetic algorithm, the parameters of the probability integral method are inverted by integrating the edge subsidence monitoring data and profile leveling data, yielding theoretical subsidence values. The average coherence coefficient and maximum deformation are calculated according to deformation gradient theory, which are then used as thresholds to fuse the SBAS-InSAR monitoring data with the probability integral method results, thereby obtaining accurate and comprehensive mining area surface subsidence results. Taking a mining area in Henan as the research case, the accuracy of this method is validated using 228 Sentinel-1 images and profile leveling data collected from April 2015 to December 2023. The results demonstrate that this approach can compensate for the limitations of SBAS-InSAR and the probability integral method. The subsidence trend aligns well with the leveling data, and the combined data, except for a few feature points, can be well matched with the leveling measurements. The root mean square error (RMSE) is 28.27 mm, lower than the errors of SBAS-InSAR and the probability integral method, indicating that the method has superior monitoring performance and can be effectively applied to obtain precise basin-wide subsidence information in mining areas.