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Title: Machine Learning in Seismology: Turning Data into Insights

Journal Article · · Seismological Research Letters
DOI: https://doi.org/10.1785/0220180259 · OSTI ID:1492617
 [1]; ORCiD logo [2];  [3];  [4];  [5];  [4]
  1. Univ. of California, Berkeley, CA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  4. University of California San Diego, La Jolla, CA (United States)
  5. Harvard Univ., Cambridge, MA (United States)
+ Show Author Affiliations

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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; Laboratory Directed Research and Development (LDRD)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1492617
Report Number(s):
LA-UR--18-28089
Journal Information:
Seismological Research Letters, Journal Name: Seismological Research Letters Journal Issue: 1 Vol. 90; ISSN 0895-0695
Publisher:
Seismological Society of AmericaCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (9)

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review journal June 2020
High-resolution seismic tomography of Long Beach, CA using machine learning journal October 2019
A Deeper Look into ‘Deep Learning of Aftershock Patterns Following Large Earthquakes’: Illustrating First Principles in Neural Network Physical Interpretability
  • 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
book May 2019
One neuron versus deep learning in aftershock prediction journal October 2019
A Neural Network for Automated Quality Screening of Ground Motion Records from Small Magnitude Earthquakes journal November 2019
Pervasive Foreshock Activity Across Southern California journal August 2019
Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault journal July 2019
Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault text January 2019
Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective journal October 2019

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