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Machine learning based analysis of the Guy-Greenbrier, Arkansas earthquakes: a tale of two sequences
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  • Yongsoo Park,
  • S. Mostafa Mousavi,
  • Weiqiang Zhu,
  • William L. Ellsworth,
  • Gregory C. Beroza
Yongsoo Park
Stanford University

Corresponding Author:[email protected]

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S. Mostafa Mousavi
Stanford University
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Weiqiang Zhu
Stanford University
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William L. Ellsworth
Stanford University
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Gregory C. Beroza
Stanford University
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Abstract

We revisited the June, 2010 - October, 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is comprised of many small clusters that exhibit diffusion along the fault at shorter time scales. Our study demonstrates that machine learning based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.
28 Mar 2020Published in Geophysical Research Letters volume 47 issue 6. 10.1029/2020GL087032