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Title: Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

Abstract

Correctly determining the association of seismic phases across a network is crucial for developing accurate earthquake catalogs. Nearly all established methods use travel time information as the main criterion for determining associations, and in problems in which earthquake rates are high and many false arrivals are present, many standard techniques may fail to resolve the problem accurately. As an alternative approach, in this work we apply convolutional neural networks (CNNs) to the problem of associations; we train CNNs to read earthquake waveform arrival pairs between two stations and predict the binary classification of whether the two waveforms are from a common source or different sources. Applying the method to a large training dataset of previously cataloged earthquakes in Chile, we obtain >80% true positive prediction rates for high-frequency data (>2 Hz) and stations separated in excess of 100 km. As a secondary benefit, the output of the neural network can also be used to infer predicted phase types of arrivals. Here, the method is ideally applied in conjunction with standard travel-time-based association routines and can be adapted for arbitrary network geometries and applications, so long as sufficient training data are available.

Authors:
 [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1492542
Report Number(s):
LA-UR-18-29660
Journal ID: ISSN 0895-0695
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Seismological Research Letters
Additional Journal Information:
Journal Volume: 90; Journal Issue: 2A; Journal ID: ISSN 0895-0695
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Seismology, Machine Learning

Citation Formats

McBrearty, Ian W., Delorey, Andrew A., and Johnson, Paul A. Pairwise Association of Seismic Arrivals with Convolutional Neural Networks. United States: N. p., 2019. Web. doi:10.1785/0220180326.
McBrearty, Ian W., Delorey, Andrew A., & Johnson, Paul A. Pairwise Association of Seismic Arrivals with Convolutional Neural Networks. United States. doi:10.1785/0220180326.
McBrearty, Ian W., Delorey, Andrew A., and Johnson, Paul A. Wed . "Pairwise Association of Seismic Arrivals with Convolutional Neural Networks". United States. doi:10.1785/0220180326.
@article{osti_1492542,
title = {Pairwise Association of Seismic Arrivals with Convolutional Neural Networks},
author = {McBrearty, Ian W. and Delorey, Andrew A. and Johnson, Paul A.},
abstractNote = {Correctly determining the association of seismic phases across a network is crucial for developing accurate earthquake catalogs. Nearly all established methods use travel time information as the main criterion for determining associations, and in problems in which earthquake rates are high and many false arrivals are present, many standard techniques may fail to resolve the problem accurately. As an alternative approach, in this work we apply convolutional neural networks (CNNs) to the problem of associations; we train CNNs to read earthquake waveform arrival pairs between two stations and predict the binary classification of whether the two waveforms are from a common source or different sources. Applying the method to a large training dataset of previously cataloged earthquakes in Chile, we obtain >80% true positive prediction rates for high-frequency data (>2 Hz) and stations separated in excess of 100 km. As a secondary benefit, the output of the neural network can also be used to infer predicted phase types of arrivals. Here, the method is ideally applied in conjunction with standard travel-time-based association routines and can be adapted for arbitrary network geometries and applications, so long as sufficient training data are available.},
doi = {10.1785/0220180326},
journal = {Seismological Research Letters},
issn = {0895-0695},
number = 2A,
volume = 90,
place = {United States},
year = {2019},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
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