A two-step algorithm for acoustic emission event discrimination based on recurrent neural networks

• Published in: Computers & geosciences Vol. 163; p. 105119
• Main Authors: Kolář, Petr, Petružálek, Matěj
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2022
• Subjects: Acoustic emission; acoustics; algorithms; Automatic seismic event processing; data collection; Event detection and localization; geophysics; neural networks; prediction; Recurrent neural network; Acoustic emission; Event detection and localization; Recurrent neural network; Automatic seismic event processing
• ISSN: 0098-3004
• DOI: 10.1016/j.cageo.2022.105119

We present an algorithm for seismic event discrimination and event approximate location based on multi-station seismograms. A deep learning approach was applied using a two-step algorithm: (i) signal onsets were identified in individual tracks based on the use of long-short-term memory neural network layers; (ii) if a sufficient number of onsets were reliably identified, a preliminary location was determined. We adopted a “reverse location approach” where the time sense of a seismogram is reverted and the origin time is predicted using a neural network approach based on previously determined onsets. Successful location or origin time prediction also served as a feedback for confirming previous onset identification. The method was tested using a data set of Acoustic Emission generated from the uniaxial loading of a Westerly Granite specimen. Accuracy of the method was better than 97%. Discriminated events were automatically located and their seismic moment tensor was determined. Both types of results were in good agreement with the baseline data set. With respect to the particular nature of processed data, we provide a demo code which shows examples presented in the article. In addition, a detailed description of the algorithm, including the control parameter values, is provided in the text. Based on this information the method can be applied on any data. •A two-step RNN algorithm for laboratory acoustic emission event discrimination.•Better than 97% discrimination accuracy.•Single-channel signal onset NN picker.•NN prediction of event localization from multi-channel rejoined onsets.•Semi-automatic seismic moment tensor and localization estimations.