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A Deep Earthquake Catalog for Oklahoma and Southern Kansas Reveals Extensive Basement Fault Networks
  • Yongsoo Park,
  • Gregory C. Beroza,
  • William L. Ellsworth
Yongsoo Park
Stanford University

Corresponding Author:[email protected]

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Gregory C. Beroza
Stanford University
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William L. Ellsworth
Stanford University
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Abstract

The successful application of deep learning for seismic phase arrival time picking has increased the efficacy of earthquake catalog development workflows. Earthquake catalogs with lower magnitude of completeness and better locational precision than current standard practice can now be generated with very limited need for human review and without the need for earthquake templates, which are not always available. Here, we report on a ‘Deep Earthquake Catalog’ with over 300,000 events from a geographically extensive region spanning Oklahoma and Southern Kansas from January 2010 to December 2020 developed using a workflow that leverages deep learning for phase picking. The increased number of events and improved spatial resolution compared to the previous statewide catalogs reveals numerous discrete faults and both broad trends and localized patterns of seismicity. This rich dataset provides new opportunities for data-driven analyses of induced earthquakes.