Machine Learning Aspects of the MyShake Global Smartphone Seismic Network

Abstract

Research Article| December 05, 2018 Machine Learning Aspects of the MyShake Global Smartphone Seismic Network 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 Asaf Inbal; Asaf Inbal bDepartment of Geophysics, Tel Aviv University, Ramat‐Aviv, Tel‐Aviv 69978, Israel Search for other works by this author on: GSW Google Scholar Richard M. Allen; Richard M. Allen cBerkeley Seismological Laboratory, University of California, Berkeley, 279 McCone Hall, Berkeley, California 94720 U.S.A. Search for other works by this author on: GSW Google Scholar Qin Lv; Qin Lv dDepartment of Computer Science, University of Colorado Boulder, 430 UCB, Boulder, Colorado 80309 U.S.A. Search for other works by this author on: GSW Google Scholar Arno Puder Arno Puder eComputer Science Department, San Francisco State University, 1600 Holloway Avenue, San Francisco, California 19132 U.S.A. Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (2A): 546–552. https://doi.org/10.1785/0220180309 Article history first online: 05 Dec 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, Asaf Inbal, Richard M. Allen, Qin Lv, Arno Puder; Machine Learning Aspects of the MyShake Global Smartphone Seismic Network. Seismological Research Letters 2018;; 90 (2A): 546–552. doi: https://doi.org/10.1785/0220180309 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 gives an overview of machine learning (ML) applications in MyShake—a crowdsourcing global smartphone seismic network. Algorithms from classification, regression, and clustering are used in the MyShake system to address various problems, such as artificial neural network (ANN) and convolutional neural network (CNN) to distinguish earthquake motions, spatial–temporal clustering using density‐based spatial clustering of applications with noise (DBSCAN) to detect earthquakes from phone aggregated information, and random forest regression to learn from existing physics‐based relationships. Beyond existing efforts, this article also presents a vision of the role of ML in some new directions and challenges. Using MyShake as an example, this article demonstrates the promising combination of ML and seismology. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.