  As the number of seismic sensors grows, it is becoming increasingly difficult
for analysts to pick seismic phases manually and comprehensively, yet such
efforts are fundamental to earthquake monitoring. Despite years of improvements
in automatic phase picking, it is difficult to match the performance of
experienced analysts. A more subtle issue is that different seismic analysts
may pick phases differently, which can introduce bias into earthquake
locations. We present a deep-neural-network-based arrival-time picking method
called "PhaseNet" that picks the arrival times of both P and S waves. Deep
neural networks have recently made rapid progress in feature learning, and with
sufficient training, have achieved super-human performance in many
applications. PhaseNet uses three-component seismic waveforms as input and
generates probability distributions of P arrivals, S arrivals, and noise as
output. We engineer PhaseNet such that peaks in probability provide accurate
arrival times for both P and S waves, and have the potential to increase the
number of S-wave observations dramatically over what is currently available.
This will enable both improved locations and improved shear wave velocity
models. PhaseNet is trained on the prodigious available data set provided by
analyst-labeled P and S arrival times from the Northern California Earthquake
Data Center. The dataset we use contains more than seven million waveform
samples extracted from over thirty years of earthquake recordings. We
demonstrate that PhaseNet achieves much higher picking accuracy and recall rate
than existing methods.
