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• W4317715418 abstract "Abstract Earthquake focal mechanisms, determined with P‐wave polarities and S/P amplitude ratios, are primary data for analyzing fault zone geometry, sense of slip, and the crustal stress field. Solving for the focal mechanisms of small earthquakes is often challenging because phase arrivals and first‐motion polarities are hard to be separated from noise. To overcome this challenge, we implement convolutional‐neural‐network algorithms (Ross, Meier, & Hauksson, 2018, Ross, Meier, Hauksson, & Heaton, 2018, https://doi.org/10.1029/2017jb015251 , https://doi.org/10.1785/0120180080 ) to detect additional phases and polarities. Using both existing and these new data, we build a high‐quality focal mechanism catalog of 297,478 events that occurred from 1981 to 2021 in southern California with the HASH method of Hardebeck and Shearer (2002), https://doi.org/10.1785/0120010200 , Hardebeck and Shearer (2003), https://doi.org/10.1785/0120020236 . The new focal mechanism catalog is overall consistent with the standard catalog (Yang et al., 2012, https://doi.org/10.1785/0120110311 ) but includes 40% more focal mechanisms, and is more consistent with moment tensor solutions derived using waveform‐fitting methods. We apply the new catalog to identify changes in focal mechanism properties caused by the occurrences of large mainshocks such as the 2010 M w 7.2 El Mayor‐Cucapah and 2019 M w 7.1 Ridgecrest earthquakes. Such changes may be associated with co‐seismic stress drops, post‐seismic deformation processes, and static stress changes on a regional scale. The new high‐resolution catalog will contribute to improved understanding of the crustal stress field, earthquake triggering mechanisms, fault zone geometry, and sense of slip on the faults in southern California." @default.
• W4317715418 created "2023-01-23" @default.
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• W4317715418 date "2023-02-01" @default.
• W4317715418 modified "2023-09-26" @default.
• W4317715418 title "Refined Earthquake Focal Mechanism Catalog for Southern California Derived With Deep Learning Algorithms" @default.
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• W4317715418 doi "https://doi.org/10.1029/2022jb025975" @default.
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