Hybrid Recognition Model of Microseismic Signals for Mining Based on Mel Spectrum and LSTM-DCNN

doi:10.12068/j.issn.1005-3026.2023.10.015

Abstract

Abstract: Microseismic monitoring can ensure safe production in mines， and the accuracy of microseismic signal recognition directly affects the analysis of microseismic events. The microseismic monitoring data of Xiadian Gold Mine were used as samples to establish a mining microseismic signal recognition model based on Mel spectrum and a combination of long short-term memory(LSTM)and deep convolutional neural networks(DCNN). Firstly， the monitoring signal was preprocessed， and the Mel time spectrum was used to reduce the weight of the interference frequency band and sample size. Then， LSTM and DCNN models were employed to extract the temporal and spatial features of the signal， respectively. Through comparative analysis of multiple models， the results showed that the proposed Mel-LSTM-DCNN hybrid model has the highest accuracy in identifying microseismic signals. The model proposed provides reference for accurately identifying microseismic signals in mines.

Key words: neural network; deep learning; signal identification; microseismic monitoring; Mel spectrum

CLC Number:

TD76

ZHAO Yong， JIAO Shi-hui， ZHAO Qian-bai. Hybrid Recognition Model of Microseismic Signals for Mining Based on Mel Spectrum and LSTM-DCNN[J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1481-1489.

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