Three-Dimensional Deformation Field Inversion Based on Fused Monitoring Data of GNSS and InSAR: A Case Study of Jinchuan No. 2 Mining Area

Surface rock movement can lead to geological or environmental problems such as surface subsidence, ground fissure development, and deformation of engineering structures, and its evolution process exhibits significant spatiotemporal heterogeneity. Therefore, conducting high-precision, spatiotemporally continuous monitoring of surface deformation is of great significance for revealing subsidence mechanisms, assessing potential risks, and guiding disaster reduction decisions. GNSS and InSAR have become mainstream methods for monitoring surface deformation, but they still have limitations in terms of spatial sparsity, 3D deformation inversion capability, and data gaps in areas of strong deformation. To address these issues, this paper takes the Jinchuan copper-nickel mine’s No. 2 mining area as the research object and comprehensively utilizes multi-source monitoring data from GNSS and InSAR to construct a joint inversion model of the surface 3D deformation field based on posterior variance component estimation, achieving adaptive optimization of weight allocation and collaborative solution of 3D deformation. To address the issue of InSAR decorrelation in areas of strong deformation, which leads to missing deformation information, a fitting and estimation approach was applied to supplement six decorrelated points that spatially coincide with GNSS stations. These points are located in key deformation areas, and their reconstruction effectively improves the completeness and reliability of the deformation field in critical regions. Based on this, an automated solution process for the fusion model is implemented using MATLAB R2022b, and the joint inversion yields spatiotemporally continuous 3D deformation fields in the northward, eastward, and vertical directions. The results show that compared with traditional monitoring methods, the proposed fusion model exhibits higher inversion accuracy and stability under different InSAR technology conditions, effectively suppressing the impact of single data source errors on the overall solution results. Among them, SBAS-InSAR shows slightly higher accuracy in the vertical direction, while PS-InSAR achieves higher accuracy in the planar direction, as indicated by lower RMSE and MAE values. The research results improve the accuracy and reliability of surface deformation monitoring in mining areas, providing important technical support for safe mining and refined management.