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

    157 Downloads (Pure)

    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

    Fingerprint

    Dive into the research topics of 'Acoustic emission waveform picking with time delay neural networks during rock deformation laboratory experiments'. Together they form a unique fingerprint.

    Cite this