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.

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Cited by in corpus (4)

• Predicting Fault Slip via Transfer Learning
• Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault
• Earthquake Arrival Association with Backprojection and Graph Theory
• Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress