Tracking earthquake sequences in real time: Application of Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP) to the 2019 Ridgecrest, California sequence

Abstract

Recent improvements in seismic data processing techniques have enhanced our ability to detail the evolution of major earthquake sequences in space and time. One such advance is new scanning algorithms that allow large volumes of waveform data to be analysed automatically, removing human biases and inefficiencies that inhibit standardized monitoring. The Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP) workflow has previously been shown to be capable of producing high-quality earthquake catalogues for injection-induced seismicity monitoring. In this study, we modify the original S-SNAP workflow to enable it to delineate the spatiotemporal distribution of major earthquake sequences in real time. We apply it to the 2019 Ridgecrest, southern California earthquake sequence, which culminated in an Mw 6.4 foreshock on July 4 and an Mw 7.1 main shock on July 6 and generated tens of thousands of smaller earthquakes. Our catalogue-which spans the period 2019 June 1 to July 16-details the spatiotemporal evolution of the sequence, including early foreshocks on July 1 and accelerating foreshocks on July 4, a seismicity gap before the main shock around its epicentre, seismicity on discrete structures within a broad fault zone and triggered earthquakes outside the main fault zone. We estimate the accuracy and false detection rate of the S-SNAP catalogue based on the reviewed catalogue reported by Southern California Seismic Network (SCSN) and our own visual inspection. We demonstrate the advantages of S-SNAP over a generalized automatic earthquake monitoring software, Seiscomp3, and a customized real-time earthquake information system for southern California, TriNet. In comparison, the S-SNAP catalogue contains five times more events than the Seiscomp3 catalogue and 1.4-2.2 times as many events per hour as the TriNet catalogue at most times. In addition, S-SNAP is more likely to solve phase association ambiguities correctly and provide a catalogue with consistent quality through time. S-SNAP would be beneficial to both routine network operations and the earthquake review process.