## 3 Data Processing of PS-InSAR  
In the data processing, as shown in Figure 7, the first step involves converting datasets into a format readable using SARPROZ(r) software. Various tasks such as calculating dataset statistics, incorporating precise orbit data if available, automatically downloading and inserting weather data for each SAR scene into the analysis, and selecting a subset of images to cover the AOI are performed in these very initial steps. Within the co-registration process, offsets between the master and slave images are estimated by investigating the correlation of amplitude information in the spatial and spectral domains. In the next stage, each slave image is aligned to the master so that the portion of imaged terrain for each pixel in the slave image corresponds to the same pixels in the master image in PS-InSAR. The selection of the master image is conducted to ensure optimal InSAR coherence across the entire stack and within the barycentre of the distribution of normal/temporal baselines. From the co-registered stack of images, the reflectivity map, as the temporal average of the intensities of all images in the dataset, is generated, and the Amplitude Stability Index (ASI) is computed. Preliminary geocoding using 30 m-SRTM DEM data is then performed. Very precise geolocation of each PS point could be obtained due to proprietary procedures implemented in SARPROZ(r). Next, the DEM in SAR coordinates is calculated to assist in PS-InSAR processing [1; 22; 45; 51; 52; 53; 54; 55; 56; 57; 58; 59]. Twenty ascending-direction CSK SAR images between 15 December 2017 and 5 December 2019 were analyzed. Additionally, 88 images were retrieved in descending and ascending directions, spanning from 11 December 2017 to 13 November 2020, for S1-B/T65 (Track 65), as represented in Figure 8. However, the first two-year period of this S1-B/T65 analysis was considered to capture the period of the other datasets and the diagnostic train in this study. In the last dataset, 54 ascending-direction S1-B/T160 (Track 160) SAR images between 5 December 2017 and 7 December 2019 were analyzed. The temporal differences between datasets were kept quite low so that the differences in terms of deformation values would not increase too much (Table 3). In detail, a total of 20 images from CSK obtained were tried to be spread over a period of two years, taking into account the archive status and the budget availability. Analyses were conducted over an area of around 800 km\({}^{2}\) inside the province of Konya to examine the deformation of a 30 km railway line and its surroundings using three different radar datasets covering a similar region, as seen in Figure 9. The research region was restricted to  
Figure 7: Simplified workflow of PS-InSAR processing in SARPROZ® (adapted from [58; 59]). In this work, a standard PS-InSAR approach [40] was adopted to estimate and remove the APS from every image's phase signal. Briefly, the APS was fitted to the outputs of the displacement velocity model. For each PS, the LOS velocity, displacement time series and height were computed relative to a reference point identified in a more stable area outside the basin/Konya city centre, as shown in Figure 9. After conducting experimental analyses and reviewing previous studies in the region [26; 27; 28; 29; 30; 31], it was determined that the reference point should be located outside Konya, particularly in mountainous areas. This will minimize the impact of subsidence displacements in and around the city center, as seen in Figures 9 and 10. Furthermore, this reference point selection process involved evaluating sites with better temporal coherence values in comparison to others. The reflectivity map image represents the topography of the study area and contains the amplitude and phase information for each pixel of the SLC image [49].