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.