A nearly 20-year hiatus in major seismic activity in southern California ended on 4 July 2019 with a sequence of intersecting earthquakes near the city of Ridgecrest, California. This sequence included a foreshock with a moment magnitude (Mw) of 6.4 followed by a Mw 7.1 mainshock nearly 34 hours later. Geodetic, seismic, and seismicity data provided an integrative view of this sequence, which ruptured an unmapped multiscale network of interlaced orthogonal faults. This complex fault geometry persists over the entire seismogenic depth range. The rupture of the mainshock terminated only a few kilometers from the major regional Garlock fault, triggering shallow creep and a substantial earthquake swarm. The repeated occurrence of multifault ruptures, as revealed by modern instrumentation and analysis techniques, poses a formidable challenge in quantifying regional seismic hazards.

We analyzed Synthetic Aperture Radar (SAR) images from Copernicus Sentinel-1A and 1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency and Global Navigation Satellite System (GNSS) data from the Network of the Americas for the 4 July 2019 Mw 6.4 and 5 July (local; 6 July UTC) Mw 7.1 Ridgecrest earthquakes. We integrated geodetic measurements for the 3D vector field of coseismic surface deformation for the two events, using SAR data from Sentinel-1 and ALOS-2 satellites. We combined 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 (Interferometric Synthetic Aperture Radar [InSAR]) measurements in the radar line of sight (LoS) direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. We also estimated the coseismic deformation for the two earthquakes from time-series processing of continuous Global Navigation Satellite System data stations in the area. 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 20 mm in the radar LoS along a short section directly south of the main rupture. About 3 km northwest of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 m of down-drop. Further northwest is a wide zone of complex deformation.

Interferometric synthetic aperture radar (InSAR) time series are sensitive to uniform horizontal and vertical plate motions• Such motions create long-wavelength spatial gradients that are visible in InSARderived velocity maps after removing other signals• Plate motion effects can be easily accounted for using plate motion models, allowing long-wavelength deformation to be more clearly seen

Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth's surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth's surface. In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster. We quantify Earth surface change using time series of interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on preevent coherence time series, and then forecasts a probability distribution of the coherence between pre-and post-event SAR images. The difference between the forecast and observed coevent coherence provides a measure of confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local behavior of coherence through time before the event. We apply this method to calculate estimates of damage for three earthquakes using multiyear time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with observed damage and quantitative improvement compared to using preto co-event coherence loss as a damage proxy.

ABSTRACT Shaking from the 6 July 2019 Mw 7.1 Ridgecrest, California, mainshock was strongly felt through southern California, but generated relatively minimal structural damage in Ridgecrest. We consider the extent to which a damage proxy map (DPM) generated from satellite-based Synthetic Aperture Radar images can detect minor damage throughout the town of Ridgecrest. The DPM does not, as expected, detect all minor structural damage to individual structures, nor can it distinguish between structural damage and earthquake-related movement that is not consequential. However, the DPM does confirm many instances of minor structural damage to larger structures and groups of smaller structures and in some instances suggests minor structural damage that is not apparent upon visual inspection. Although ambiguous identification of minor damage may not be useful to guide earthquake response, the identification of minor, possibly hidden damage is potentially useful for other purposes. Overall, the DPM confirms that structural damage was commensurate with modified Mercalli intensity no higher than 7 throughout Ridgecrest. We consider both instrumental and intensity data to explore further the distribution of near-field ground motions over the frequency range of engineering concern. Peak ground accelerations and peak ground velocities estimated from “Did You Feel It?” intensity data using the Worden et al. (2012) ground-motion intensity conversion equation (GMICE) are consistent with recorded instrumental data. Both instrumental and estimated mainshock peak accelerations are further consistent with predictions from both the Boore et al. (2014) ground-motion prediction equation (GMPE), but lower than predicted by the Atkinson and Wald (2007) and Atkinson et al. (2014) intensity prediction equations (IPEs). A GMPE such as Boore et al. (2014), which is constrained by a large global dataset, together with a well-constrained GMICE, may thus characterize expected shaking intensities for large earthquakes better than an IPE based on more limited intensity data.