A Spatio-Temporal Fusion Method for Mine Subsidence Based on Decoupling the Basic Deformation Field from Residuals Using InSAR-GNSS

Ground surface subsidence resulting from underground mining is characterized by significant spatial deformation gradients and nonlinear temporal deformation. A single monitoring technique is insufficient to simultaneously achieve extensive spatial coverage and high temporal continuity. Interferometric Synthetic Aperture Radar (InSAR) provides high spatial resolution but is limited by sparse temporal sampling, whereas the Global Navigation Satellite System (GNSS) offers dense temporal sampling but with restricted spatial coverage. To address these limitations, this study introduces a spatio-temporal fusion method combining InSAR and GNSS, based on baseline deformation field–residual decoupling (BR-STKF), specifically applied to the Banji Coal Mine in the Huainan Mining Area, Anhui Province. Sentinel-1 SBAS-InSAR is employed to derive Line-Of-Sight (LOS) deformation time series, while three-dimensional GNSS displacements are projected onto the LOS direction. A daily scale baseline deformation field is constructed using pixel-wise Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing. Subsequently, residual signals between the InSAR/GNSS observations and the baseline field are estimated utilizing a correlation-anchored multiscale spatial basis and an information-form Kalman filter. Helmert variance component estimation is introduced to adaptively adjust the weights of InSAR and GNSS observations. To demonstrate the reliability of the proposed framework, a quantitative validation was conducted. The experimental results yielded root mean square error (RMSE) values of 6.37 mm for strict spatial leave-one-out cross-validation (LOOCV) and 2.84 mm for retrospective reconstruction with missing InSAR phases. These results are competitive and comparable to leading benchmarks such as the Spatio-Temporal Kalman Filter (STKF), while achieving lower mean RMSEs than baseline methods such as Spatio-Temporal Random Effects (STREs), Enhanced Spatio-Temporal Random Effects (eSTREs), Improved Spatio-Temporal Random Effects (ISTREs), Multi-resolution Orthogonal Polynomial Basis (MOP), and ST-Kriging. However, the statistical stability of differences among models requires further verification with additional independent data. The findings suggest that the baseline deformation field–residual decoupling strategy effectively integrates the spatial coverage of InSAR with daily GNSS observations, offering a viable framework for daily scale line-of-sight (LOS) deformation reconstruction in mining areas.