Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness

Lubbers, Nicholas Edward; Bolton, David C.; Mohd-Yusof, Jamaludin; Marone, Chris; Barros, Kipton Marcos; Johnson, Paul A.

doi:10.1029/2018GL079712

Title: Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness

Journal Article · 12 November 2018 · Geophysical Research Letters

DOI: https://doi.org/10.1029/2018GL079712 · OSTI ID:1482951

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Pennsylvania State Univ., University Park, PA (United States)

Abstract Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. Here we show that a similar approach is successful using event catalogs derived from the continuous data. Our methods are applicable to catalogs of arbitrary scale and magnitude of completeness. We investigate how machine learning regression from an event catalog of laboratory earthquakes performs as a function of the catalog magnitude of completeness. We find that strong model performance requires a sufficiently low magnitude of completeness, and below this magnitude of completeness, model performance saturates.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001; EE0006762

OSTI ID:: 1482951

Alternate ID(s):: OSTI ID: 1491929

Report Number(s):: LA-UR-18-26559

Journal Information:: Geophysical Research Letters, Vol. 45, Issue 24; ISSN 0094-8276

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 31 works

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Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault Ren, C. X.; Dorostkar, O.; Rouet-Leduc, B. arXiv https://doi.org/10.48550/arxiv.1903.11157	text	January 2019

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