Diverse algorithms have been developed for efficient earthquake signal processing and characterization. These algorithms are becoming increasingly important as seismologists strive to extract as much insight as possible from exponentially increasing volumes of continuous seismic data. Deep neural networks have been shown to be promising tools for this. We have developed a number of deep learning tools for more efficient processing and characterizing of earthquake signals. In my presentation, I demonstrate the performance of some of these tools applied to seismic data. AI-based techniques have the potential to improve our monitoring ability and as a result understanding of earthquake processes and hazards.

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.