High‐resolution interseismic velocity data along the San Andreas Fault from GPS and InSAR

Abstract

[1] We compared four interseismic velocity models of the San Andreas Fault based on GPS observations. The standard deviations of the predicted secular velocity from the four models are larger north of the San Francisco Bay area, near the creeping segment in Central California, and along the San Jacinto Fault and the East California Shear Zone in Southern California. A coherence spectrum analysis of the secular velocity fields indicates relatively high correlation among the four models at longer wavelengths (>15-40 km), with lower correlation at shorter wavelengths. To improve the short-wavelength accuracy of the interseismic velocity model, we integrated interferometric synthetic aperture radar (InSAR) observations, initially from Advanced Land Observing Satellite (ALOS) ascending data (spanning from the middle of 2006 to the end of 2010, totaling more than 1100 interferograms), with GPS observations using a Sum/Remove/Filter/Restore approach. The final InSAR line of sight data match the point GPS observations with a mean absolute deviation of 1.5 mm/yr. We systematically evaluated the fault creep rates along major faults of the San Andreas Fault and compared them with creepmeters and alignment array data compiled in Uniform California Earthquake Rupture Forecast, Version 2 (UCERF2). Moreover, this InSAR line of sight dataset can constrain rapid velocity gradients near the faults, which are critical for understanding the along-strike variations in stress accumulation rate and associated earthquake hazard.

Pore pressure sensitivities to dynamic strains: Observations in active tectonic regions

“…This type of spatial pattern suggests a dependence on rates of secular shear strain accumulation across the fault, which in the case of the SAF can vary significantly along strike: profiles of strain are expected to be broader in the locked section than in the creeping section, where they are expected to be focused at the fault owing to shallow creep. Gradients in geodetic velocity profiles [Shen et al, 2011;Tong et al, 2013] highlight this expected pattern.…”

“…Figure 5 shows the observations, the maximum likelihood estimates of the scaling relationship, and the Bayesian posterior distribution discussed above, separated by station. The geometry of the these clusters facilitates inspection of variations in response mostly at similar distances from the fault, and Figure 6 shows regression coefficients along a great circle roughly parallel to the strike of the fault, comparing them to interseismic creep rates compiled by Tong et al [2013].…”

Constraining crustal velocity fields with InSAR for Eastern Turkey: Limits to the block‐like behavior of Eastern Anatolia

“…While the earliest InSAR interseismic studies were based on small stacks of interferograms [e.g., Wright et al, 2001], current such studies commonly involve analysis of hundreds of interferograms spanning several overlapping tracks and across regions hundreds of kilometers wide [e.g., Jolivet et al, 2012;Kaneko et al, 2013;Tong et al, 2013;Cavalié and Jónsson, 2014]. New SAR missions such as Sentinel-1, with regular acquisitions and frequent revisit times, will enable InSAR to be used to map crustal deformation on the global scale, and to be incorporated into maps of global strain rate, which are currently based on GPS measurements alone [e.g., Kreemer et al, 2003].…”

Analysis of Ocean Tide Loading in Differential InSAR Measurements

“…DiCaprio and Simons [6] discussed the importance of OTL correction in DInSAR based on an empirical ocean tide model, and they showed the superiority of an ocean tide model in correcting the OTL effect in DInSAR. Some studies acknowledged the OTL effect in DInSAR measurements as a potential error source and tried to use empirical models to correct it [7][8][9]. However, whether or not an ocean tide model can be used for spatial analysis is still unclear, and needs to be verified with real data.…”