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Acoustic Emission event onsets recognized by Neural Network formalism

Authors

Kolář,  Petr
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Petružálek,  Matěj
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Jechumtálová,  Zuzana
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Šílený,  Jan
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Lokajíček,  Tomáš
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Kolář, P., Petružálek, M., Jechumtálová, Z., Šílený, J., Lokajíček, T. (2023): Acoustic Emission event onsets recognized by Neural Network formalism, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0836


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016625
Abstract
One of the contemporary trend in seismology is process huge data sets automatically with use of Neural Network (NN) formalism. We present seismogram onsets interpretation obtained both by Convolution NN as well as Recurrent NN approach. We investigated data from Acoustic Emission loading experiment with Westerly Granite. Such data appeared to be suitable for testing of NN approach as they are more homogeneous then data originated from natural earthquakes, but simultaneously they are complex enough not to be of trivial interpretation. We designed NN architecture, learned in and compare the results with biased interpretation. We were searching not only for onsets on individual seismograms but we try to identified the whole events. In addition to automatic onsets identification we (also automatically) determined event location and seismic moment tensor. Comparison with biased data proved that these automatically obtained values can be successfully used as preliminary estimation at least. Problems of multiple events identification are discussed as well. The method has a potential to be applicable on natural earthquake seismograms.