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

By Weiqiang Zhu, Gregory C. Beroza. Geophysical Journal International, 2018

1,143 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/5ae0f6a3b5fc882ce0b05ff1e8f333caf2e0549e

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Citations in the corpus

CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection (2018, background): “There are, however, two main problems that occur in the training of very deep networks (where “deep” is usually taken to mean more than 15 layers): (1) vanishing/exploding gradients and (2) degradation (saturation and degradation of accuracy).”
Machine learning for data-driven discovery in solid Earth geoscience (2019, background): “SeveralrecentstudieshaveappliedDNNswith various architectures for automatic earthquake and seismic event detection ( 16 , 68 , 69 ), phase-picking ( 66 , 70 ), and classification of volcano-seismic events ( 71 ).”
Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers (2021, background): “PhaseNet (Zhu & Beroza, 2019) is a U-Net based model for arrival time picking.”
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, methodology): “For the ‘‘autopicker’’ picks we use only arrival times with high conﬁdence (high probabilities given by the deep-learning model [4]).”
Earthquake Phase Association Using a Bayesian Gaussian Mixture Model (2021, background): “We applied the PhaseNet model (W. Zhu & Beroza, 2018) to extract picks from waveforms of “HH,” “BH,” “EH,” and “HN” channels of 23 stations of the “CI” network within 1 degree of the location (−117.504 W, 35.705 N).”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, methodology): “Deep learning has been shown to be a powerful tool for learning the characteristics of seismic data [47, 48, 49, 50, 51, 52, 53, 54].”