Automating the Detection of Dynamically Triggered Earthquakes via a Deep Metric Learning Algorithm

Vivian Tang*, Prem Seetharaman, Kevin Chao, Bryan A. Pard, Suzan Van Der Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Detecting subtle signals from small earthquakes triggered by transient stresses from the surface waves of large magnitude earthquakes can contribute to a more general understanding of how earthquakes nucleate and interact with each other. However, searching for signals from such small earthquakes in thousands of seismograms is overwhelming, and discriminating them from a miscellany of noise is challenging. Here, we explore how we can automate the detection of such dynamically triggered earthquakes using a simple, diagnostic signal-to-noise ratio (SNR) threshold as well as a convolutional deep metric learning network. Our analysis shows that the deep learning network was more reliable at detecting small earthquakes than the SNR method.

Original languageEnglish (US)
Pages (from-to)901-912
Number of pages12
JournalSeismological Research Letters
Volume91
Issue number2
DOIs
StatePublished - Mar 1 2020

ASJC Scopus subject areas

  • Geophysics

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