Estimating Fault Friction from Seismic Signals in the Laboratory

TL;DR

This study demonstrates that seismic signals in laboratory fault experiments can be used to accurately estimate fault friction and stress state using machine learning and signal analysis, providing a new way to monitor fault dynamics.

Contribution

The paper introduces a novel method to infer fault friction and stress from seismic signals using machine learning, applicable in laboratory settings.

Findings

Seismic signals follow a pattern related to fault friction.

Machine learning can identify the fault's failure cycle stage.

A simple equation relates seismic power to fault friction.

Abstract

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing, but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. These results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.