## 3 Data Sets and Methods
### Permanent Scatterers Analyses of InSAR Pairs  
In order to detect subtle ground deformation over the target area, a number of error components of the DInSAR phase have to be dealt with using proper error suppression techniques. The main error source is the electromagnetic phase delay by troposphere water vapor turbulence, (i.e., the wet delay component). The stratified phase delay, called the dry delay component, is not significant owing to the relatively low topographic relief of the target area. The target area is located on the eastern coastal line and experiences large seasonal precipitation variations (seven times higher in summer than winter). This phenomenon is attributed to orographic effects, where the seasonal unequaled water vapor distribution dominates in the eastern coastal area of the Korean Peninsula. These effects are caused by the topographic profile and by water vapor from the eastern sea, and they frequently mislead the interpretation of InSAR processing [43] and need to be regulated. In InSAR time series analysis, stacking multiple interferograms helps estimate and reduce errors from water vapor and from an inaccurate base DEM. The InSAR time series technique was largely classified by Permanent Scatterers (PS) by [PERSON] et al. [44,45] and Small Baseline Subsets (SBAS) by [PERSON] et al. [46] according to the InSAR pair connection methods (i.e., 1 to 1 connection in PS and many to many in SBAS). The algorithmic improvements of time series analysis mainly aim for the densification of reliable scatterers and have proven capabilities [47]. However, according to a preliminary PS InSAR analysis, it appeared that the temporal and spatial baseline conditions between SAR images in this study were adequate to address those technical challenges with an ordinary PS algorithm, as shown in Figure 2. It was also observed that the density of extracted scatterers was sufficient for further interpretation (see Figure 3). This is because the densi  
Figure 2: Acquisition time (**a**) and connection graphs of employed Sentinel-1 InSAR pairs (**b**,**c**). In total, 60 ascending and 86 descending images were employed for time series analysis. Note: the red box in (**a**) represents the target period of decomposition. of the target area are optimal for producing stable scatterers, which are essential for the success of PS algorithms. The error terms that need to be suppressed by PS algorithms can be expressed as follows:  
\[\Phi_{\text{E}}=\Phi_{\text{a}}+\Phi_{\text{o}}+\Phi_{\text{t}}+\Phi_{\text{n}} \tag{1}\]  
\[\Phi_{\text{a}}=\Phi_{\text{tropospheric}}+\Phi_{\text{ionospheric}} \tag{2}\]  
where \(\Phi_{\text{E}}\) is the phase difference by whole error factors, \(\Phi_{\text{a}}\) is the phase difference by atmospheric error terms, \(\Phi_{\text{o}}\) is the phase difference by inaccurate orbital information, \(\Phi_{\text{t}}\) is the phase difference by inaccurate DEM, \(\Phi_{\text{n}}\) is the phase noise, \(\Phi_{\text{tropospheric}}\) is the phase difference by tropospheric components, and \(\Phi_{\text{ionospheric}}\) is the phase difference by delay in the ionosphere. Because the target has a relatively flat topography, which is ideal for successful inversion of PS processing, it was theorized that PS algorithms could effectively suppress major error components. However, the temporal gap between the image acquisitions and the construction time of the base DEM, almost 20 years in the case of SRTM DEM usage, can pose complications for the application of PS algorithms over heavily rebuilt topography (e.g., that of the target area). Therefore, we employed the ALOS PRISM DEM, which was extracted from a 2005-2010 stereo mission [48], as the base DEM of PS processing. In addition to improving temporal coverage, the ALOS PRISM DEM has better vertical accuracy over the central city area owing to its intrinsic characteristics of 2.5 m resolution stereo matching compared to interferogram processing with a 15 m resolution in the SRTM resolution.