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[SCG55-05] Postseismic Displacement of the 2022 Chihshang Earthquake in Eastern Taiwan Detected by InSAR and GNSS

Keywords:InSAR, GNSS, earthquake, fault, Taiwan, postseismic deformation
First, we investigated the displacement field of each station for one year after the Chihshang earthquake using GNSS daily coordinates obtained by Precise Point Positioning (PPP). The result (Figure a) shows a displacement discontinuity along the LVF (a westside uplift of up to 10 cm and a left-lateral displacement of about 10 cm). This GNSS displacement pattern is clearly different from the steady-state convergence of about 2 cm/yr and slight westside subsidence observed along the LVF before the earthquake. Since the hypocentral area is located in the mountains with limited GNSS data, we therefore used InSAR data to increase the spatial resolution of surface displacements. To penetrate the dense vegetation in eastern Taiwan, we used the L-band SAR data acquired by ALOS-2. We then applied the Split Spectrum Method (Gomba et al., 2016; Wegmüller et al., 2018) to mitigate the strong influence of the ionospheric disturbances. The tropospheric disturbances were also corrected using GACOS (Yu et al., 2018). After these corrections, we stacked 39 interferograms (e.g., Figure b) created from 17 ScanSAR data taken from September 2022 to December 2023. The results show that (1) displacement discontinuity across the LVF, (2) satellite line-of-sight (LOS) shortening over the vast area to the west of CRF, (3) lack of the LOS length change at the main rupture area of the Chihshang earthquake estimated by Tang et al. (2023). The second characteristic (2) would be mainly due to the postseismic slip of the CRF and partly by the inelastic flow of the surrounding rocks. These characteristics provide the keys to quantitatively understanding the stress distribution before the Chihshang earthquake and the kinematic interaction between LVF and CRF. In the future, we will construct a kinematic model that explains the temporal evolution of GNSS and InSAR data, by which we quantitatively understand the stress build-up and re-distribution on LVF and CRF.