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Pairwise association of seismic arrivals with convolutional neural networks

Ian W. McBrearty, Andrew A. Delorey and Paul A. Johnson
Pairwise association of seismic arrivals with convolutional neural networks
Seismological Research Letters (January 2019) 90 (2A): 503-509

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 (>2Hz) and stations separated in excess of 100km. As a secondary benefit, the output of the neural network can also be used to infer predicted phase types of arrivals. 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.


ISSN: 0895-0695
EISSN: 1938-2057
Serial Title: Seismological Research Letters
Serial Volume: 90
Serial Issue: 2A
Title: Pairwise association of seismic arrivals with convolutional neural networks
Affiliation: Los Alamos National Laboratory, Geophysics Group, Los Alamos, NM, United States
Pages: 503-509
Published: 20190109
Text Language: English
Publisher: Seismological Society of America, El Cerrito, CA, United States
References: 27
Accession Number: 2019-014622
Categories: Seismology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table, sketch map
S56°00'00" - S17°45'00", W76°00'00" - W67°00'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Abstract, Copyright, Seismological Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 201910
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