Journal Article

Phase Neural Operator for Multi‐Station Picking of Seismic Arrivals

Zhu, Weiqiang, Sun, Hongyu, Azizzadenesheli, Kamyar, Ross, Zachary E.

2023

Overview

Seismic wave arrival time measurements form the basis for numerous downstream applications. State‐of‐the‐art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic data by examining the whole network jointly. Here, we introduce a general‐purpose network‐wide phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our model, called Phase Neural Operator, leverages the spatio‐temporal contextual information to pick phases simultaneously for any seismic network geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking more phase arrivals, while also greatly improving measurement accuracy. Following similar trends being seen across the domains of artificial intelligence, our approach provides but a glimpse of the potential gains from fully‐utilizing the massive seismic data sets being collected worldwide. Plain Language Summary Earthquake monitoring often involves measuring arrival times of P‐ and S‐waves of earthquakes from continuous seismic data. With the advancement of artificial intelligence, state‐of‐the‐art phase picking methods use deep neural networks to examine seismic data from each station independently; this is in stark contrast to the way that human experts annotate seismic data, in which waveforms from the whole network containing multiple stations are examined simultaneously. With the performance gains of single‐station algorithms approaching saturation, it is clear that meaningful future advances will require algorithms that can naturally examine data for entire networks at once. Here we introduce a multi‐station phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our algorithm, called Phase Neural Operator, leverages the spatial‐temporal information of earthquake signals from an input seismic network with arbitrary geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking many more seismic wave arrivals, yet also greatly improving measurement accuracy. Key Points We introduce a multi‐station phase picking algorithm, Phase Neural Operator (PhaseNO), that is based on a new machine learning paradigm called Neural Operator PhaseNO can use data from any number of stations arranged in any arbitrary geometry to pick phases across the input stations simultaneously By leveraging the spatial and temporal contextual information, PhaseNO achieves superior performance over leading baseline algorithms

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Accuracy / Algorithms / Arrivals / Artificial intelligence / Artificial neural networks / earthquake / Earthquakes / Learning algorithms / Machine learning / natural hazards / Neural networks / neural operators / Operators (mathematics) / P-waves / phase picking / S waves / Saturation / Seismic activity / Seismic data / seismic monitoring / Seismic waves / Seismograms / Seismological data / Waveforms