Estimating Fault Friction From Seismic Signals in the Laboratory

• Published in: Geophysical research letters Vol. 45; no. 3; pp. 1321 - 1329
• Main Authors: Rouet‐Leduc, Bertrand, Hulbert, Claudia, Bolton, David C., Ren, Christopher X., Riviere, Jacques, Marone, Chris, Guyer, Robert A., Johnson, Paul A.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 16.02.2018; American Geophysical Union
• Subjects: Acoustics; Artificial intelligence; Earth; Earth Sciences; earthquake hazard; Earthquakes; Equations of state; fault friction; Fault lines; Fault zones; Fingerprints; Friction; GEOSCIENCES; Laboratories; laboratory earthquake; Learning algorithms; Machine learning; Mathematical models; P-waves; Physical properties; Physical states; Seismic activity; seismic signal identification; Seismic waves; Shear stress
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1002/2017GL076708

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. Plain Language Summary In a laboratory setting that closely mimics Earth faulting, we show that the most important physical properties of a fault can be accurately estimated using machine learning to analyze the sound that the fault broadcasts. The artificial intelligence identifies telltale sounds that are characteristic of the physical state of the fault, and how close it is to failing. A fundamental relation between the sound emitted by the fault and its physical state is thus revealed. Key Points Machine learning models can discern the frictional state of a laboratory fault from the statistical characteristics of the seismic signal The use of machine learning uncovers an equation of state linking fault friction and statistical characteristics of the seismic signal The discovery of this equation of state also uncovers the hysterectic behavior of the laboratory fault