Machine Learning in Seismology: Turning Data into Insights

doi:https://doi.org/10.1785/0220180259

Title: 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:

Kong, Qinkai ^[1];

^[2]; Ross, Zachary E. ^[3]; Bianco, Michael J. ^[4]; Meade, Brendan J. ^[5]; Gerstoft, Peter ^[4]

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:: 2018-11-14

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:: Journal Article: 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.

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                    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.  https://doi.org/10.1785/0220180259

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                    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.  https://doi.org/10.1785/0220180259.  https://www.osti.gov/servlets/purl/1492617.

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@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},

  url          = {https://www.osti.gov/biblio/1492617},
  journal      = {Seismological Research Letters},

  issn         = {0895-0695},

  number       = 1,

  volume       = 90,

  place        = {United States},

  year         = {2018},

  month        = {11}

}

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Cited by: 29 works

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Works referencing / citing this record:

A Neural Network for Automated Quality Screening of Ground Motion Records from Small Magnitude Earthquakes
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Bellagamba, Xavier; Lee, Robin; Bradley, Brendon A.
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A Neural Network for Automated Quality Screening of Ground Motion Records from Small Magnitude Earthquakes
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Bellagamba, Xavier; Lee, Robin; Bradley, Brendon A.
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A Deeper Look into ‘Deep Learning of Aftershock Patterns Following Large Earthquakes’: Illustrating First Principles in Neural Network Physical Interpretability
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Mignan, Arnaud; Broccardo, Marco; Rojas, Ignacio
Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part I, p. 3-14
https://doi.org/10.1007/978-3-030-20521-8_1

Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault
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Ren, C. X.; Dorostkar, O.; Rouet‐Leduc, B.
Geophysical Research Letters, Vol. 46, Issue 13
https://doi.org/10.1029/2019gl082706

Pervasive Foreshock Activity Across Southern California
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Geophysical Research Letters, Vol. 46, Issue 15
https://doi.org/10.1029/2019gl083725

One neuron versus deep learning in aftershock prediction
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Mignan, Arnaud; Broccardo, Marco
Nature, Vol. 574, Issue 7776
https://doi.org/10.1038/s41586-019-1582-8

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
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Xie, Yazhou; Ebad Sichani, Majid; Padgett, Jamie E.
Earthquake Spectra
https://doi.org/10.1177/8755293020919419

Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective
journal, October 2019

Karimzadeh, Sadra; Matsuoka, Masashi; Kuang, Jianming
ISPRS International Journal of Geo-Information, Vol. 8, Issue 10
https://doi.org/10.3390/ijgi8100462

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Title: Machine Learning in Seismology: Turning Data into Insights

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