Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction. Issue 8 (11th August 2020)

• Record Type: Journal Article
• Authors: Bolton, David C.; Shreedharan, Srisharan; Rivière, Jacques; Marone, Chris
• Is Part Of: Journal of geophysical research. Volume 125:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-11
• Subjects: Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2019JB018975
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo‐ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time‐dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide‐hold‐slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning‐based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure. Key Points: Coseismic energy release during laboratory earthquakes scales directly with stress drop Acoustic energy radiated throughout the lab seismic cycle tacks fault slip rate and depends on contact junction size Acoustic energy from laboratory foreshocks and mainshocks derives from breaking and sliding of frictional contact junctions