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

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ID: 302680
2018
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Abstract
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 the probability distributions provide accurate arrival times for both P and S waves. PhaseNet is trained on the prodigious available data set provided by analyst-labelled P and S arrival times from the Northern California Earthquake Data Center. The data set we use contains more than 700 000 waveform samples extracted from over 30 yr of earthquake recordings. We demonstrate that PhaseNet achieves much higher picking accuracy and recall rate than existing methods when applied to the waveforms of known earthquakes, which has 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.
Reference Key
openalex_W2794417179 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Weiqiang Zhu, Gregory C. Beroza
Journal geophysical journal international
Year 2018
DOI
10.1093/gji/ggy423
URL
https://academic.oup.com/gji/article-pdf/216/1/261/26329430/ggy423.pdf
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