Machine Learning in Seismology: Turning Data into Insights

Review Article| November 14, 2018 Machine Learning in Seismology: Turning Data into Insights Qingkai Kong; Qingkai Kong aBerkeley Seismological Laboratory, University of California, Berkeley, 209 McCone Hall, Berkeley, California 94720 U.S.A., kongqk@berkeley.edu Search for other works by this author on: GSW Google Scholar Daniel T. Trugman; Daniel T. Trugman bLos Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A. Search for other works by this author on: GSW Google Scholar Zachary E. Ross; Zachary E. Ross cSeismological Laboratory, California Institute of Technology, Pasadena, California 91125 U.S.A. Search for other works by this author on: GSW Google Scholar Michael J. Bianco; Michael J. Bianco dScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093‐0238 U.S.A. Search for other works by this author on: GSW Google Scholar Brendan J. Meade; Brendan J. Meade eDepartment of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138 U.S.A. Search for other works by this author on: GSW Google Scholar Peter Gerstoft Peter Gerstoft dScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093‐0238 U.S.A. Search for other works by this author on: GSW Google Scholar Author and Article Information Qingkai Kong aBerkeley Seismological Laboratory, University of California, Berkeley, 209 McCone Hall, Berkeley, California 94720 U.S.A., kongqk@berkeley.edu Daniel T. Trugman bLos Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545 U.S.A. Zachary E. Ross cSeismological Laboratory, California Institute of Technology, Pasadena, California 91125 U.S.A. Michael J. Bianco dScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093‐0238 U.S.A. Brendan J. Meade eDepartment of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138 U.S.A. Peter Gerstoft dScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093‐0238 U.S.A. Publisher: Seismological Society of America First Online: 14 Nov 2018 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (1): 3–14. https://doi.org/10.1785/0220180259 Article history First Online: 14 Nov 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Qingkai Kong, Daniel T. Trugman, Zachary E. Ross, Michael J. Bianco, Brendan J. Meade, Peter Gerstoft; Machine Learning in Seismology: Turning Data into Insights. Seismological Research Letters 2018;; 90 (1): 3–14. doi: https://doi.org/10.1785/0220180259 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 This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.