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 Marone, Kipton Barros, Paul A. Johnson

Research output: Contribution to journalArticle

7 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Earth and Planetary Sciences(all)

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