Characterization of Acoustic Emissions From Analogue Rocks Using Sparse Regression‐DMDc

Abstract

The measurement and analysis of acoustic emissions is a non-destructive, passive-monitoring technique that is used to determine changes in the physical and chemical conditions of a rock. An acoustic emission (AE) is defined as a transient elastic wave generated by the rapid release of energy within a material (Lockner, 1993;Scruby, 1987). After energy release, AE emanates from the location, or zone, of abrupt and localized mechanical and interfacial energy that triggered the generation of the elastic waves (Scruby, 1987). In geophysics, acoustic emissions methods have been used to monitor crack initiation, coalescence, and propagation (Lockner, 1993), shearing behavior along fractures (Bolton et al., 2020;Rouet-Leduc et al., 2018), and the evolution of drying fronts (Moebius et al., 2012). More recently, acoustic emissions from transportable sources have been used to

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

“…In the controlled laboratory setting, on the other hand, accurate labels of the physical system are generated at the same time as the laboratory earthquakes. Fieseler et al (2022) apply an unsupervised sparse regression model to classify acoustic emission signals related to different cracking mechanisms and suggest using the differences in the reconstruction accuracy as an indicator for classification. By focusing the attention of the neural networks on specific features, fracture loading mode (Z. and fracture saturation are successfully inferred from the laboratory earthquakes.…”

“…Fieseler et al. (2022) apply an unsupervised sparse regression model to classify acoustic emission signals related to different cracking mechanisms and suggest using the differences in the reconstruction accuracy as an indicator for classification. By focusing the attention of the neural networks on specific features, fracture loading mode (Z.…”