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As the number of seismic sensors grows, it is becoming increasingly difficultfor analysts to pick seismic phases manually and comprehensively, yet suchefforts are fundamental to earthquake monitoring. Despite years of improvementsin automatic phase picking, it is difficult to match the performance ofexperienced analysts. A more subtle issue is that different seismic analystsmay pick phases differently, which can introduce bias into earthquakelocations. We present a deep-neural-network-based arrival-time picking methodcalled "PhaseNet" that picks the arrival times of both P and S waves. Deepneural networks have recently made rapid progress in feature learning, and withsufficient training, have achieved super-human performance in manyapplications. PhaseNet uses three-component seismic waveforms as input andgenerates probability distributions of P arrivals, S arrivals, and noise asoutput. We engineer PhaseNet such that peaks in probability provide accuratearrival times for both P and S waves, and have the potential to increase thenumber of S-wave observations dramatically over what is currently available.This will enable both improved locations and improved shear wave velocitymodels. PhaseNet is trained on the prodigious available data set provided byanalyst-labeled P and S arrival times from the Northern California EarthquakeData Center. The dataset we use contains more than seven million waveformsamples extracted from over thirty years of earthquake recordings. Wedemonstrate that PhaseNet achieves much higher picking accuracy and recall ratethan existing methods.

PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method