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Alexander Berne

and 3 more

Eric Fielding

and 9 more

The 4 July 2019 Mw 6.4 Earthquake and 5 July Mw 7.1 Earthquake struck near Ridgecrest, California. Caltech-Jet Propulsion Laboratory Advanced Rapid Imaging and Analysis (ARIA) project automatically processed synthetic aperture radar (SAR) images from Copernicus Sentinel-1A and -1B satellites operated by the European Space Agency, and products were delivered to the US and California Geological Surveys to aid field response. We integrate geodetic measurements for the three-dimensional vector field of coseismic surface deformation for thee two events and measure the early postseismic deformation, using SAR data from Sentinel-1 satellites and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by Japanese Aerospace Exploration Agency. We combine less precise large-scale displacements from SAR images by pixel offset tracking or matching, including the along-track component, with the more precise SAR interferometry (InSAR) measurements in the radar line-of-sight direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. InSAR coherence and coherence change maps the surface disruptions due to fault ruptures reaching the surface. Large slip in the Mw 6.4 earthquake was on a NE-striking fault that intersects with the NW-striking fault that was the main rupture in the Mw 7.1 earthquake. The main fault bifurcates towards the southeast ending 3 km from the Garlock Fault. The Garlock fault had triggered slip of about 15 mm along a short section directly south of the main rupture. About 3 km NW of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 meter of down-drop. Further NW is a wide zone of complex deformation. We image postseismic deformation with InSAR data and point measurements from new GPS stations installed by the USGS. Initial analysis of the first InSAR measurements indicates the pull-apart started rebounding in the first weeks and the main fault had substantial afterslip close to the epicenter where the largest coseismic slip occurred. Slip on a NE-striking fault near the northern end of the main rupture in the first weeks, in the same zone as large and numerous aftershocks along NE-striking and NW-striking trends shows complex deformation.

Tobias Köhne

and 2 more

Regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks are a powerful tool to monitor plate motion and surface deformation. Since their inception, their size, density, and length of observation record have steadily increased throughout the world. Simultaneously, researchers have had to write accompanying software to enable the analysis (especially the decomposition) of the ever-increasing amount of available timeseries in an efficient way. These codes and respective studies have individually set standards for different subsets of the following desirable qualities: portability (between locations), speed (code runtime), automation (avoiding or simplifying manual inspection of each station), use of spatial correlation (exploiting the fact that stations experience common signals), availability (open source), and documentation (of the usage and underlying methods). In this study, we present the DISSTANS Python package, which aims to combine the aforementioned achievements in a single software by offering generic (therefore portable), parallelizable (fast) methods that can exploit the spatial structure of the observation records in a user-assisted, semi-automated framework, including uncertainty propagation. The code is open source, includes an application interface documentation as well as usage tutorials, is easily extendable, and is based on the previously published and validated method of Riel et al. (2014). We also present two case studies to validate our code, one using a synthetic dataset and one using real GNSS network timeseries.