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

SUMMARY 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.

Automatic Phase‐Picking Method for Detecting Earthquakes Based on the Signal‐to‐Noise‐Ratio Concept

The automatic phase‐picking detection of earthquakes is a challenge under the background of big data and strong noise circumstances. The short‐term average/long‐term average (STA/LTA) ratio is widely used to detect earthquake due to its simplicity and robustness. However, STA/LTA‐based methods may not perform well with noisy data. Based on the signal‐to‐noise‐ratio (SNR) concept, a short‐term power/long‐term power (STP/LTP) ratio method is proposed. The characteristic function and the detection thresholds of the STP/LTP method are given physical meanings. Through a sample analysis, the STP/LTP detection results of both the P and S phases are better than the results of the STA/LTA by means of mean deviation, standard deviations, distributions of detection results, error rate, and missed rate on different SNR levels. In general, the STP/LTP method inherits the simple characteristics of the STA/LTA method, and it is suitable for phase picking of low‐SNR seismic data.

Automatic phase pickers: Their present use and future prospects

abstract Automatic phase-picking algorithms are designed to detect a seismic signal on a single trace and to time the arrival of the signal precisely. Because of the requirement for precise timing, a phase-picking algorithm is inherently less sensitive than one designed only to detect the presence of a signal, but still can approach the performance of a skilled analyst. A typical algorithm filters the input data and then generates a function characterizing the seismic time series. This function may be as simple as the absolute value of the series, or it may be quite complex. Event detection is accomplished by comparing the function or its short-term average (STA) with a threshold value (THR), which is commonly some multiple of a long-term average (LTA) of a characteristic function. If the STA exceeds THR, a trigger is declared. If the event passes simple criteria, it is reported. Sensitivity, expected timing error, false-trigger rate, and false-report rate are interrelated measures of performance controlled by choice of the characteristic function and several operating parameters. At present, computational power limits most systems to one-pass, time-domain algorithms. Rapidly advancing semi-conductor technology, however, will make possible much more powerful multi-pass approaches incorporating frequency-domain detection and pseudo-offline timing.