Machine‐Learning‐Based Analysis of the Guy‐Greenbrier, Arkansas Earthquakes: A Tale of Two Sequences
Yongsoo Park, S. Mostafa Mousavi, Weiqiang Zhu, William L. Ellsworth, Gregory C. Beroza
Abstract
Abstract We revisited the June 2010 to 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 composed of many small clusters that exhibit diffusion along the fault at shorter timescales. Our study demonstrates that machine‐learning‐based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.