Machine Learning in Seismology: Turning Data into Insights
Abstract
In this article, we provide 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. Lastly, we conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.
- Authors:
-
[2];
- Univ. of California, Berkeley, CA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- California Inst. of Technology (CalTech), Pasadena, CA (United States)
- University of California San Diego, La Jolla, CA (United States)
- Harvard Univ., Cambridge, MA (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1492617
- Report Number(s):
- LA-UR-18-28089
Journal ID: ISSN 0895-0695
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Seismological Research Letters
- Additional Journal Information:
- Journal Volume: 90; Journal Issue: 1; Journal ID: ISSN 0895-0695
- Publisher:
- Seismological Society of America
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Kong, Qinkai, Trugman, Daniel Taylor, Ross, Zachary E., Bianco, Michael J., Meade, Brendan J., and Gerstoft, Peter. Machine Learning in Seismology: Turning Data into Insights. United States: N. p., 2018.
Web. doi:10.1785/0220180259.
Kong, Qinkai, Trugman, Daniel Taylor, Ross, Zachary E., Bianco, Michael J., Meade, Brendan J., & Gerstoft, Peter. Machine Learning in Seismology: Turning Data into Insights. United States. doi:10.1785/0220180259.
Kong, Qinkai, Trugman, Daniel Taylor, Ross, Zachary E., Bianco, Michael J., Meade, Brendan J., and Gerstoft, Peter. Wed .
"Machine Learning in Seismology: Turning Data into Insights". United States. doi:10.1785/0220180259. https://www.osti.gov/servlets/purl/1492617.
@article{osti_1492617,
title = {Machine Learning in Seismology: Turning Data into Insights},
author = {Kong, Qinkai and Trugman, Daniel Taylor and Ross, Zachary E. and Bianco, Michael J. and Meade, Brendan J. and Gerstoft, Peter},
abstractNote = {In this article, we provide 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. Lastly, we conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.},
doi = {10.1785/0220180259},
journal = {Seismological Research Letters},
number = 1,
volume = 90,
place = {United States},
year = {2018},
month = {11}
}
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