## 2 InSAR Coseismic Deformation and Postseismic Time Series
### Postseismic Observations and Time Series  
Compared to coseismic deformation, postseismic deformation with relatively low amplitude is known to be difficult to observe. The transient deformation after a strong earthquake usually lasts from several months to several decades but rapidly decays over time [13; 31; 43]. The InSAR technique is a crucial method for studying the temporal and spatial characteristics of transient postseismic displacements resulting from this event over a 450-day period. To mitigate the phase decorrelation caused by rugged topography and dense vegetation in mountainous areas, we adopted the Persistent Scatterer InSAR (PS-InSAR) approach [44; 45] to extract the postseismic time series during the 450 days following the 2021 Maduo mainshock. This was implemented by the Stanford Method for Persistent Scatterers (StaMPS version 4.1b). The PS-InSAR approach comprehensively utilizes the phase noise and amplitude dispersion of each pixel to identify stable Persistent Scatterers (PS) points [44]. Detailed data processing procedures for the StaMPS software (version 4.1b) are shown in Appendix A. We selected a multilook factor of five looks in range and one look in azimuth for the interferograms to ensure a sufficient number of candidate PS points. Initially, we registered the slave images with a single master image and generated a series of interferograms using the GAMMA software (version 1.0-October 2014) (Figure S1). We chose the master image for the ascending orbit from 10 November 2021 and for the descending orbit from 22 November 2021. All SAR images were used, with no limitations on the temporal and spatial baselines (Figure S1). The observations were then converted into the input files required for StaMPS. Finally, we implemented a 3D phase unwrapping to derive continuous displacement using the SNAPHU algorithm [45]. During InSAR data processing, orbit errors and atmospheric anomalies have a significant impact on the extraction of the low-amplitude deformation following a large earthquake [13; 31]. Therefore, the Sentinel-1 A/B precision orbit data were utilized prior to the master-to-slave image co-registration ([[https://dataspace.copernicus.eu](https://dataspace.copernicus.eu)]([https://dataspace.copernicus.eu](https://dataspace.copernicus.eu)), accessed on 28 May 2024). Generally, orbit error appears as a long-wavelength signal intermingled in the InSAR observations, manifesting as a phase ramp in the interferograms, which can be modeled by a quadratic polynomial [39]. Another significant error source in InSAR deformation measurement is atmospheric inhomogeneity, which results in path delay anomalies and significantly affects the accuracy of small-scale deformation observations [13]. Atmospheric anomalies consist of tropospheric and ionospheric artifacts, with tropospheric artifacts being more challenging to remove. Tropospheric artifacts can generally be divided into turbulence effects and stratification effects [46; 47]. The atmospheric stratification effect on an interferogram can be mitigated by establishing the relationship between the elevation and phase delays [13; 47]. In the temporal domain, the turbulence effect manifests as a high-frequency signal, while it appears as a low-frequency signal in the spatial domain. According to the spatio-temporal characteristics of turbulence, we adopt a combination of low- and high-pass filters to effectively reduce the turbulence noise [31; 41]. The ionospheric artifact is primarily wavelength-dependent in this study, and its impact on C-band Sentinel-1A/B observations can generally be ignored. The observed displacements after the 2021 Maduo earthquake may contain several signals, including linear tectonic velocities, seasonal variations due to hydrological and atmospheric loadings, and transient postseismic deformation. To extract the 'pure' postseismic time series, we first have to subtract these undesired signals.