GNSS-corrected InSAR displacement time-series spanning the 2019 Ridgecrest, CA earthquakes

Abstract

Summary InSAR displacement time series are emerging as a valuable product to study a number of earth processes. One challenge to current time series processing methods, however, is that when large earthquakes occur, they can leave sharp coseismic steps in the time series. These discontinuities can cause current atmospheric correction and noise smoothing algorithms to break down, as these algorithms commonly assume that deformation is steady through time. Here, we aim to remedy this by exploring two methods for correcting earthquake offsets in InSAR time series: a Simple Difference Offset Estimate (SDOE) process and a Multiparameter Offset Estimate (MPOE) parametric time series inversion technique. We apply these methods to a two-year time series of Sentinel-1 interferograms spanning the 2019 Ridgecrest, CA earthquake sequence. Descending track results indicate that the SDOE method precisely corrects for only 20 per cent of the coseismic offsets at 62 study locations included in our scene and only partially corrects or sometimes overcorrects for the rest of our study sites. On the other hand, the MPOE estimate method successfully corrects the coseismic offset for the majority of sites in our analysis. This MPOE method allows us to produce InSAR time series and data-derived estimates of deformation during each phase of the earthquake cycle. In order to better isolate and estimate the signal of postseismic lithospheric deformation in the InSAR time series, we apply a GNSS-based correction to our interferograms. This correction ties the interferograms to median-filtered weekly GNSS displacements and removes additional atmospheric artifacts. We present InSAR-based estimates of postseismic deformation for the area around the Ridgecrest rupture, as well as a two-year coseismic-corrected, GNSS-corrected InSAR time series dataset. This GNSS-corrected InSAR time series will enable future modeling of postseismic processes such as afterslip in the near field of the rupture, poroelastic deformation at intermediate distances, and viscoelastic deformation at longer time scales in the far field.

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

“…No surface ruptures were identified in the field, and the aftershocks could not characterize the seismogenic fault [23]. Given this, searching for the optimal geometrical fault parameters using non-linear inversion, integrated with the distribution of aftershocks and local geological structures, provides a plausible way to probe seismogenic structures [24][25][26][27]. To do so, we employed a non-linear inversion algorithm, implemented in the Geodetic Bayesian Inversion Software (GBIS, Version 1.1) [28], to build the maximum posterior probabilities of fault parameters (i.e., length, depth, width, strike, dip, position, and slip).…”

“…No surface ruptures were identified in the field, and the aftershocks could not characterize the seismogenic fault [23]. Given this, searching for the optimal geometrical fault parameters using non-linear inversion, integrated with the distribution of aftershocks and local geological structures, provides a plausible way to probe seismogenic structures [24][25][26][27].…”

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

“…GNSS sites are mostly deployed with deep anchors in bedrock or anchored through reinforced concrete structures and stable buildings (Xi et al 2021;Xu et al 2022); thus, these monitoring sites have fixed location properties. Furthermore, their errors can be suppressed by performing multisite combination networking calculations and error modeling (Dai et al 2019;Guns et al 2022). However, InSAR technology is limited by its own imaging and positioning principles, resulting in a lack of an absolute reference frame for monitoring data, while the achieved monitoring precision is easily affected by factors such as orbital and atmospheric errors (Yang et al 2020;Lee and Shirzaei 2023).…”