Automatic Extraction of a Generalized Physics‐Based Feature From Time‐Lapse Active Source Ultrasonic Waveforms for Prediction of Shear Stress in Laboratory Friction Experiments

Abstract

Abstract Physics‐based handcrafted features, such as P‐wave amplitude derived from early parts of active source ultrasonic waveforms, successfully predict shear stress evolution. This study investigates whether feature extraction can be automated by leveraging full active source ultrasonic waveforms recorded during a laboratory friction experiment. We compare four models: handcrafted feature‐driven model (reference), convolutional neural network (CNN) model, explainable data‐driven (XDD) model, and a model with an explainable generalized physics‐based (XPG) feature. The reference model uses the spectral amplitude of the P‐wave packet calculated at center frequency, while the CNN relies on convolutional and pooling layers for feature extraction. The XDD model combines CNN feature extraction with long short‐term memory units and an attention layer to capture feature time history and significance. The novel XPG extracts a generalized spectral amplitude feature through a custom multilayer perceptron designed to learn linear combination of signals' spectral amplitudes without prescribing specific frequencies. Although CNN‐extracted features resemble handcrafted ones, both the CNN and XDD models underperform compared to the reference, while the XPG model outperforms all others. The attention mechanism in the XDD model highlights the importance of first P‐wave packet, whereas the XPG model also identifies later‐arriving shear wave packet as significant. The prediction improvement by inclusion of shear waves is physically intuitive; S‐waves have particle motion polarized in the direction of fault shear and are likely more sensitive to frictional state than P‐waves. This work demonstrates the value of incorporating domain knowledge into automatic feature extraction with potential applications in seismic data analysis.