Seismic Phase Picking

TL;DR

This paper reviews traditional and learning-based methods for automatic seismic phase picking, a crucial task in earthquake monitoring, addressing the challenges posed by increasing seismic data volume.

Contribution

It systematically explores various approaches to automatic seismic phase picking, highlighting advancements and potential improvements over manual methods.

Findings

Traditional methods are limited in scalability and accuracy.

Learning-based methods show promise for improved automation.

The paper discusses the advantages and challenges of different techniques.

Abstract

Seismic phase picking, which aims to determine the arrival time of P- and S-waves according to seismic waveforms, is fundamental to earthquake monitoring. Generally, manual phase picking is trustworthy, but with the increasing number of worldwide stations and seismic monitors, it becomes more challenging for human to complete the task comprehensively. In this work, we explore multiple ways to do automatic phase picking, including traditional and learning-based methods.