Title: Laboratory earthquake forecasting: A machine learning competition

Abstract

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a modelmore » for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.« less

Authors:

Johnson, Paul A. ^[1]; Rouet-Leduc, Bertrand ^[1]; Pyrak-Nolte, Laura J.  ^[2];

• Search DOE PAGES for author "Pyrak-Nolte, Laura J."
• Search DOE PAGES for ORCID "0000-0001-6826-5214"

Beroza, Gregory C.  ^[3];

• Search DOE PAGES for author "Beroza, Gregory C."
• Search DOE PAGES for ORCID "0000-0002-8667-1838"

Marone, Chris J. ^[4]; Hulbert, Claudia ^[5]; Howard, Addison ^[6]; Singer, Philipp ^[7]; Gordeev, Dmitry  ^[7];

• Search DOE PAGES for author "Gordeev, Dmitry"
• Search DOE PAGES for ORCID "0000-0002-8888-0473"

Karaflos, Dimosthenis  ^[8];

• Search DOE PAGES for author "Karaflos, Dimosthenis"
• Search DOE PAGES for ORCID "0000-0001-7312-3237"

Levinson, Corey J.  ^[9];

• Search DOE PAGES for author "Levinson, Corey J."
• Search DOE PAGES for ORCID "0000-0002-0455-9966"

Pfeiffer, Pascal ^[10]; Puk, Kin Ming  ^[11];

• Search DOE PAGES for author "Puk, Kin Ming"
• Search DOE PAGES for ORCID "0000-0002-6432-101X"

Reade, Walter ^[6]

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+ Show Author Affiliations

1. Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545,
2. Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907,, Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN 47907,, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907,
3. Department of Geophysics, Stanford University, Stanford, CA 94305,
4. Department of Earth Science, La Sapienza Università di Roma, 00413 Rome, Italy,, Department of Earth Science, Pennsylvania State University, University Park, PA 16802,
5. Laboratoire de Géologie, Département de Géosciences, École Normale Supérieure, PSL University, CNRS UMR, 8538 Paris, France,
6. Kaggle, Google, LLC, Denver, CO 80301,
7. H2O.ai, 1010 Vienna, Austria,
8. Private individual, Athens 11364, Greece,
9. Private individual, Jacksonville, FL, 32207,
10. Department of Electrical Engineering, Rheinisch-Westfälische Technische Hochschule Aachen University, 52056 Aachen, Germany,
11. Private individual, Bethesda, MD 20817

Publication Date:

Mon Jan 25 00:00:00 EST 2021

Research Org.:

Stanford Univ., CA (United States); Pennsylvania State Univ., University Park, PA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:

1762534

Alternate Identifier(s):

OSTI ID: 1774648; OSTI ID: 1829644; OSTI ID: 1831406; OSTI ID: 1861296; OSTI ID: 2267616

Report Number(s):

LA-UR-20-28829; LA-UR-20-26035
Journal ID: ISSN 0027-8424; e2011362118

Grant/Contract Number:  

9233218CNA000001; FG02-09ER16022; SC0020445; SC0020512; EE0008763; 20200278ER; 89233218CNA000001

Resource Type:

Published Article

Journal Name:

Proceedings of the National Academy of Sciences of the United States of America

Additional Journal Information:

Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 118 Journal Issue: 5; Journal ID: ISSN 0027-8424

Publisher:

Proceedings of the National Academy of Sciences

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES; machine learning competition; laboratory earthquakes; earthquake prediction; physics of faulting; machine learning; artificial intelligence; forecasting; Earth Sciences; Seismicity