@article{3c0aac7f638e47d2ac634d6d043e957d,
title = "Earthquake Catalog-Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness",
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.",
author = "Nicholas Lubbers and Bolton, {David C.} and Jamaludin Mohd-Yusof and Chris Marone and Kipton Barros and Johnson, {Paul A.}",
note = "Funding Information: This work is supported by institutional support (LDRD) at the Los Alamos National Laboratory. We gratefully acknowledge the support of the Center for Nonlinear Studies. C. M. was supported by the National Science Foundation (NSF-EAR1520760) and the Department of Energy (DE-EE0006762). We thank Andrew Delorey, Robert A. Guyer, Bertrand Rouet-Leduc, Claudia Hulbert, and James Theiler for productive discussions. The data used here are freely available from the Penn State Rock Mechanics lab at http://www3.geosc.psu.edu/∼cjm38/. Publisher Copyright: {\textcopyright}2018. American Geophysical Union. All Rights Reserved.",
year = "2018",
month = dec,
day = "28",
doi = "10.1029/2018GL079712",
language = "English (US)",
volume = "45",
pages = "13,269--13,276",
journal = "Geophysical Research Letters",
issn = "0094-8276",
publisher = "American Geophysical Union",
number = "24",
}