Acoustic emission waveform picking with time delay neural networks during rock deformation laboratory experiments

Thomas King, Philip Benson, Luca De Siena, Sergio Vinciguerra

Research output: Contribution to journalArticlepeer-review

Abstract

We report a new method using a time delay neural network to transform acoustic emission (AE) waveforms into a time series of instantaneous frequency content and permutation entropy. This permits periods of noise to be distinguished from signals. The model is trained in sequential batches, using an automated process that steadily improves signal recognition as new data are added. The model was validated using AE data from rock deformation experiments, using Darley Dale sandstone in fully drained conditions at a confining pressure of 20 MPa (approximately 800 m simulated depth). The model is initially trained by manual picking of five high‐amplitude waveforms randomly selected from the dataset (experiment). This is followed by semisupervised training on a subset of 300 waveforms.
Original languageEnglish
Pages (from-to)923-932
JournalSeismological Research Letters
Volume92
Issue number2A
Early online date30 Dec 2020
DOIs
Publication statusPublished - 1 Mar 2021

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