Pairwise Association of Seismic Arrivals with Convolutional Neural Networks

Research Article| January 09, 2019 Pairwise Association of Seismic Arrivals with Convolutional Neural Networks Ian W. McBrearty; Ian W. McBrearty aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Search for other works by this author on: GSW Google Scholar Andrew A. Delorey; Andrew A. Delorey aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Search for other works by this author on: GSW Google Scholar Paul A. Johnson Paul A. Johnson aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Search for other works by this author on: GSW Google Scholar Author and Article Information Ian W. McBrearty aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Andrew A. Delorey aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Paul A. Johnson aGeophysics Group, Los Alamos National Laboratory, D446 P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A., imcbrearty@lanl.gov Publisher: Seismological Society of America First Online: 09 Jan 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (2A): 503–509. https://doi.org/10.1785/0220180326 Article history First Online: 09 Jan 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Ian W. McBrearty, Andrew A. Delorey, Paul A. Johnson; Pairwise Association of Seismic Arrivals with Convolutional Neural Networks. Seismological Research Letters 2019;; 90 (2A): 503–509. doi: https://doi.org/10.1785/0220180326 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search 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. 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. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.