An Improved Quadtree Sampling Method for InSAR Seismic Deformation Inversion

Abstract

With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.

Fault Kinematics of the 2023 Mw 6.0 Jishishan Earthquake, China, Characterized by Interferometric Synthetic Aperture Radar Observations

“…We downsampled the InSAR pixels for ascending and descending tracks using a quadtree approach while maximizing the resolution on the fault plane [35]. Here, the quadtree approach uses a deformation gradient threshold to divide the window into different sizes [36], which results in dense pixels in the region of large coseismic deformation and parse pixels in the far field. To account for the potential uncertainty of the fault trace, we excluded InSAR data points within 1 km of the fault.…”

Coseismic Kinematics of the 2023 Kahramanmaras, Turkey Earthquake Sequence From InSAR and Optical Data

“…Given that the fault ruptures of the 2023 Kahramanmaras earthquakes reach the surface according to the coseismic deformation measurements (Figure 2), we thus only carry out the linear inversion for the coseismic slip distribution. Before coseismic source modeling, to improve inversion efficiency, the InSAR‐derived LOS deformations and POT‐derived range and azimuth offsets are downsampled using the saliency‐based quadtree sampling algorithm (Gao et al., 2021). To weight the downsampled data, we construct a variance‐covariance matrix considering the noise structure (He et al., 2022).…”