Surface deformation monitoring of mining areas based on the fusion of D-InSAR and SBAS-InSAR using the sigmoid function: a case study of a mining area in jilin province, China

Accurate monitoring of surface subsidence in mining areas is crucial for disaster prevention. To enhance the accuracy of surface subsidence monitoring in mining areas, this study employs GNSS data as a reference and utilizes Sentinel-1A SAR imagery to observe LOS surface deformation within the study area. The study first employs both time-series cumulative D-InSAR and SBAS-InSAR techniques to observe line-of-sight (LOS) surface deformation, leveraging their complementary strengths in capturing subsidence of varying magnitudes. Furthermore, persistent scatterer points are used to reduce the cumulative errors inherent in time-series cumulative D-InSAR techniques. The core innovation of this study lies in an adaptive fusion method based on the sigmoid function, effectively integrating deformation observations from both InSAR techniques. The fusion results exhibit a maximum MAE of 31.161 mm and a RMSE of 44.985 mm, with a coefficient of determination of 0.970 when compared to GNSS monitoring data. Compared with existing data and traditional fusion methods, this fusion approach significantly enhances result accuracy, providing a solid scientific foundation for subsequent mining disaster early warning and prevention efforts.