Experimental investigation on acoustic emission precursor of rockburst based on unsupervised machine learning method

Jie Sun; Dongqiao Liu; Pengfei He; Longji Guo; Binghao Cao; Lei Zhang; Zhe Li

Rock Mechanics Bulletin (Apr 2024)

Experimental investigation on acoustic emission precursor of rockburst based on unsupervised machine learning method

Jie Sun,
Dongqiao Liu,
Pengfei He,
Longji Guo,
Binghao Cao,
Lei Zhang,
Zhe Li

Affiliations

Jie Sun: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, 100083, China
Dongqiao Liu: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; Corresponding author.
Pengfei He: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; Institute for Deep Underground Science and Engineering, China University of Mining and Technology, Beijing, 100083, China; Corresponding author. State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing China.
Longji Guo: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; Department of Earth Sciences, University of Florence, Firenze 50121, Italy
Binghao Cao: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing, 100083, China
Lei Zhang: School of Science, China University of Mining and Technology, Beijing, 100083, China
Zhe Li: State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; School of Energy and Mining Engineering, China University of Mining and Technology, Beijing, 100083, China

Journal volume & issue: Vol. 3, no. 2
p. 100099

Abstract

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The key to achieving rockburst warning lies in the understanding of rockburst precursors. Considering the correlation characteristics of rockburst acoustic emission (AE) parameters, a self-organizing map neural network (SOMNN) based method for rockburst precursor inversion was proposed. The feature of this method lies in a cyclic data segmentation iteration process based on the thinking of “interference signal screening”, “key signal extraction”, and “precursor signal inversion”. The rationality of this method has been verified in three groups of rockburst experiments. The results revealed that rockburst AE precursor signals consist of a series of signals characterized by long duration, high energy, low average frequency, high energy amplitude, and low peak frequency. Subsequently, potential value in long term rockburst warning of the precursor obtained in this study was shown via the comparison of conventional precursors. Finally, a preliminary interpretation for rockburst precursor was proposed under the framework of AE parameters physical significance, and it is revealed that AE precursor signals are likely linked to the creation of large-scale tensile cracks before rockburst.

Published in Rock Mechanics Bulletin

ISSN: 2773-2304 (Online)
Publisher: KeAi Communications Co., Ltd.
Country of publisher: China
LCC subjects: Technology: Mining engineering. Metallurgy; Technology: Hydraulic engineering
Website: https://www.keaipublishing.com/en/journals/rock-mechanics-bulletin/

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