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

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. Plain Language Summary: Seismologists analyze faults in the earth by creating earthquake catalogs‐records of the times, locations, and sizes of earthquakes. For decades, researchers have attempted to use the these catalogs to predict the timing and size of future earthquakes. Recently, researchers have found that machine learning algorithms can forecast the motion of the fault using subtle "creaking" sounds, both in the laboratory and in the real world. These creaking sounds had previously been thought to be noise and were not commonly cataloged as earthquake activity. We installed a very powerful sensor in a laboratory fault and created a very detailed catalog that captures very small quakes—small enough that they would have looked like noise to a less powerful sensor. We then used machine learning on this catalog to try and forecast the large laboratory earthquakes. We found that machine learning model is successful when small‐enough events are part of the catalog. This says that subtle seismic sounds that look like noise may be very small earthquakes that were previously overlooked. These findings suggest that to improve earthquake forecasting, we might broaden our ideas of what signals to label as potential earthquakes and save in catalogs. Key Points: Machine learning can model important characteristics of laboratory fault physics by training on finely resolved catalogs of slip eventsFault physics becomes significantly harder to learn if catalogs are truncated at or above a critical magnitude of completeness [ABSTRACT FROM AUTHOR]