Phase Neural Operator for Multi-Station Picking of Seismic Arrivals

TL;DR

This paper introduces PhaseNO, a neural operator-based model that jointly analyzes entire seismic networks to improve earthquake detection and phase picking accuracy, surpassing existing methods.

Contribution

The paper presents a novel neural operator approach for network-wide seismic phase picking, enabling simultaneous analysis of all stations for the first time.

Findings

Detects more earthquakes than baseline methods

Picks more phase arrivals with higher accuracy

Leverages spatio-temporal context for improved performance

Abstract

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 PhaseNO, 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 datasets being collected worldwide.