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

Abstract

Here, 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.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Pennsylvania State Univ., University Park, PA (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:
1482951
Report Number(s):
LA-UR-18-26559
Journal ID: ISSN 0094-8276
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Name: Geophysical Research Letters; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Information Science; Machine Learning; Laboratory earthquakes; Earthquake catalogs; Earthquake forecasting; Magnitude of completeness

Citation Formats

Lubbers, Nicholas Edward, Bolton, David C., Mohd-Yusof, Jamaludin, Marone, Chris, Barros, Kipton Marcos, and Johnson, Paul A. Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness. United States: N. p., 2018. Web. doi:10.1029/2018GL079712.
Lubbers, Nicholas Edward, Bolton, David C., Mohd-Yusof, Jamaludin, Marone, Chris, Barros, Kipton Marcos, & Johnson, Paul A. Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness. United States. doi:10.1029/2018GL079712.
Lubbers, Nicholas Edward, Bolton, David C., Mohd-Yusof, Jamaludin, Marone, Chris, Barros, Kipton Marcos, and Johnson, Paul A. Mon . "Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness". United States. doi:10.1029/2018GL079712.
@article{osti_1482951,
title = {Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness},
author = {Lubbers, Nicholas Edward and Bolton, David C. and Mohd-Yusof, Jamaludin and Marone, Chris and Barros, Kipton Marcos and Johnson, Paul A.},
abstractNote = {Here, 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.},
doi = {10.1029/2018GL079712},
journal = {Geophysical Research Letters},
number = ,
volume = ,
place = {United States},
year = {Mon Nov 12 00:00:00 EST 2018},
month = {Mon Nov 12 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
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