Large‐Scale Interseismic Strain Mapping of the NE Tibetan Plateau From Sentinel‐1 Interferometry

Abstract

Over recent decades, Interferometric Synthetic Aperture Radar (InSAR) has emerged as a powerful geodetic tool for imaging crustal deformation on the continents (e.g., Burgmann et al., 2000;Elliott et al., 2016). Numerous studies have demonstrated InSAR's ability to reveal the presence of active faults that were not previously recognized (e.g.,

Earthquake Cycle Deformation Associated With the 2021 MW 7.4 Maduo (Eastern Tibet) Earthquake: An Intrablock Rupture Event on a Slow‐Slipping Fault From Sentinel‐1 InSAR and Teleseismic Data

“…The weights are distance‐dependent and derived from a variogram model fitted to the semi‐variogram of the deviations of the data from a polynomial trend. We chose to fit a spherical instead of an exponential or Gaussian variogram model to balance smoothness and feature retention (Ou et al., 2022). To avoid artifacts due to closely‐spaced contrasting velocities of mixed quality, we interpolated only GNSS north velocities with uncertainties below 0.7 mm/yr.…”

“…We propagate the uncertainties of the interpolated GNSS north velocities through the LOS velocity decomposition by compounding the V N uncertainties with the LOS uncertainties as described in the Supporting Information S1 of Ou et al. (2022). The two velocity fields we obtained are of ∼100 m resolution, 1–2 mm/yr uncertainty, and spans an area of 205,000 km 2 (Figures S11 and S12 in Supporting Information S1).…”

“…We generate strain rate fields at 1 km sampling spacing using average velocities derived from ∼6.5 years of Sentinel-1 InSAR time series (between October 2014 and May 2021, see Figure S2 in Supporting Information S1 for the interferogram networks). We follow the method of Ou et al (2022) to derive strain rate fields by combining horizontal velocity gradients of the filtered InSAR east velocities and the interpolated GNSS north velocities (Section 2.6.2, Figure S13 in Supporting Information S1).…”

“…The kriging algorithm also uses the same variogram model to produce interpolated uncertainties that increase away from the GNSS control points. We propagate the uncertainties of the interpolated GNSS north velocities through the LOS velocity decomposition by compounding the V N uncertainties with the LOS uncertainties as described in the Supporting Information S1 of Ou et al (2022).…”

A Complete Map of Fine‐Scale Slip Rate Distribution and Earthquake Potential Along the Haiyuan Fault System

“…Jolivet et al (2012) made the first attempt to map the slip-rate distribution over a 300-km-long section only as there were a limited number of good-quality InSAR data at that time. Ou et al (2022) produced InSAR velocities for the entire Haiyuan fault zone, but they examined mainly surface strain rates. Here, we use our own Sentinel-1 InSAR rate maps derived from a more sophisticated time-series analysis (Huang et al, 2022), in combination with GPS velocities, to model rate distribution at kilometer scale.…”