## 3 Methodology  
### SBAS-InSAR  
The SBAS-InSAR method paired all the SAR images with multiple master images by setting certain temporal and spatial baseline thresholds. It then utilized the least squares method and singular value decomposition, based on high-coherence points, to obtain nonlinear surface deformation information [14; 31]. The interferometric phase information \(\varnothing_{\text{int}}\) after SAR image processing is shown in Equation (1):  
\[\varnothing_{\text{int}}=\varnothing_{\text{flat}}+\varnothing_{\text{topo}}+ \varnothing_{\text{def}}+\varnothing_{\text{atmo}}+\varnothing_{\text{noise}} \tag{1}\]  
where \(\varnothing_{\text{flat}}\) simplifies the model by assuming a flat surface. It includes contributions from the average elevation of the terrain, but does not account for specific features like lakes or other surface irregularities. The flat-earth assumption is primarily used to reduce the complexity in the phase calculations. \(\varnothing_{\text{topo}}\) represents the effects of the Earth's topography. It captures the phase changes due to the variations in elevation across the landscape, such as hills and valleys. \(\varnothing_{\text{def}}\) represents the changes in phase due to the movement of observation targets along the line of sight (LOS). It captures the actual ground deformation that is being monitored. \(\varnothing_{\text{atmo}}\) accounts for phase variations caused by atmospheric conditions, such as changes in temperature, pressure, and humidity. It reflects how the atmosphere can introduce noise into the radar signal. \(\varnothing_{\text{noise}}\) accounts for any additional noise in the measurements, which may arise from various sources not covered by the other phases. By clearly distinguishing these components, we can better understand how they contribute to the overall phase observed in the interferograms.  
In this study, we used the open-source software Mintpy version 1.5 to process the interferograms generated by HyP3. Compared to other methods, Mintpy exhibits a superior performance in low-coherence areas and high-coherence regions with phase-unwrapping errors or complex displacements [32]. Mintpy is primarily divided into two modules: (1) the correction and inversion of unwrapping errors in the raw phase time series, and (2) the correction of phase errors from different sources to obtain a displacement time series. The main steps include (1) the editing of the connectivity graph; (2) the selection of control points; (3) the unwrapping correction and network inversion; (4) a tropospheric delay correction and topographic residual correction; (5) the selection of the best reference image; (6) a noise assessment; and (7) an estimation of the average deformation rates. The ground deformation rates obtained based on the SBAS-InSAR method were the deformation results in the radar line-of-sight (LOS) direction. This study focused on LS characteristics, so horizontal displacements were ignored. The deformation information in the LOS direction was converted to vertical results using Equation (2):  
\[D_{\text{v}}=\frac{D_{\text{LOS}}}{\text{cos}\theta} \tag{2}\]  
where \(D_{\text{v}}\) is the vertical deformation rate (mm/a), \(D_{\text{LOS}}\) is the LOS deformation rate (mm/a), and \(\theta\) is the radar incidence angle.