Earthquake Catalog-Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)13,269-13,276
JournalGeophysical Research Letters
Volume45
Issue number24
DOIs
StatePublished - Dec 28 2018

Fingerprint

earthquake catalogue
machine learning
completeness
catalogs
earthquakes
regression analysis
acoustic emission
shear stress
fault zone
friction
earthquake
laboratory
effect

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Earth and Planetary Sciences(all)

Cite this

Lubbers, Nicholas ; Bolton, David C. ; Mohd-Yusof, Jamaludin ; Marone, Chris J. ; Barros, Kipton ; Johnson, Paul A. / Earthquake Catalog-Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness. In: Geophysical Research Letters. 2018 ; Vol. 45, No. 24. pp. 13,269-13,276.
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Earthquake Catalog-Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness. / Lubbers, Nicholas; Bolton, David C.; Mohd-Yusof, Jamaludin; Marone, Chris J.; Barros, Kipton; Johnson, Paul A.

In: Geophysical Research Letters, Vol. 45, No. 24, 28.12.2018, p. 13,269-13,276.

Research output: Contribution to journalArticle

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