A novel acoustic emission parameter for predicting rock failure during Brazilian test based on cepstrum analysis

Wang, Honglei; He, Xiaocong; Song, Dazhao; Guo, Haifeng

doi:10.1051/e3sconf/202019201004

Cited by 2 publications

(1 citation statement)

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“…Xu [26] studied the variations in acoustic emission event counts and duration during the fracture failure process of limestone specimens. Wang [27] investigated the frequency-domain variations in acoustic emission energy during the failure process of limestone under Brazilian splitting loading conditions. Guo [28] characterized the failure process of coal under Brazilian splitting loading conditions using acoustic emission parameters such as energy, frequency counts, and amplitude, and compared them jointly with mechanical damage laws.…”

Section: Introductionmentioning

confidence: 99%

Classification of Microseismic Signals Using Machine Learning

Chen,

Cui,

et al. 2024

Processes

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The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals.

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Section: Introductionmentioning

confidence: 99%