Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

Pingan Peng, Ru Lei () and Jinmiao Wang
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Pingan Peng: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Ru Lei: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Jinmiao Wang: School of Environment and Resources, Xiangtan University, Xiangtan 411105, China

Sustainability, 2023, vol. 15, issue 20, 1-17

Abstract: As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.

Keywords: metal mines; microseismic monitoring; deep learning; transformer; automatic classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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