Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning‐Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

Wu‐Yu Liao; En‐Jui Lee; Dawei MuPo Chen

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Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning‐Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

机译：Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning‐Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

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Accurate and (near) real‐time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U‐Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activity in southern California during the 2019 Ridgecrest sequence. The distribution of earthquakes determined by our method is consistent with that in the Southern California Earthquake Data Center (SCEDC) catalog. In addition, the number of earthquakes in our catalog is more than three times that of the SCEDC catalog. Our method identifies additional earthquakes that are close in origin times and/or locations, and are not included in the SCEDC catalog. Our algorithm avoids misidentification of seismic phases for earthquake location. In general, our algorithm can provide reliable earthquake monitoring on a large area, even during a high seismicity period.

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来源
《Seismological research letters》 |2022年第3期|1880-1894|共15页
作者
Wu‐Yu Liao; En‐Jui Lee; Dawei MuPo Chen;
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作者单位

Department of Earth Sciences, National Cheng Kung University;

National Center for Supercomputing Applications, University of Illinois at Urbana‐Champaign;

Department of Geology and Geophysics, University of Wyoming;

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正文语种英语
中图分类地震学;
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Toward Fully Autonomous Seismic Networks: Backprojecting Deep Learning‐Based Phase Time Functions for Earthquake Monitoring on Continuous Recordings

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