Artificial intelligence method for automatic classification of vibration signals in the mining process
Rui Dai, Jie Shao,*, Da Zhang, Hu Ji, and Yi Zeng
1. Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics,Chinese Academy of Sciences, Beijing, 100029
2. University of Chinese Academy of Sciences, Beijing, 100029
3. BGRIMM Technology Group Co., Ltd., Beijing, 102628
4. China - South Africa Joint Research Center for Exploitation and Utilization of Mineral Resources, Beijing, 102628
5. China - South Africa BRI Joint Laboratory for Sustainable Development and Utilization of Mineral Resources, Beijing,102628
Abstract：
The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems. Ground pressure monitoring, supported by microseismic technology, plays a pivotal role in ensuring mine safety by enabling real-time identification and accurate classification of vibration signals such as microseismic signals, blasting signals, and noise. These classifications are critical for improving the efficacy of ground pressure monitoring systems, conducting stability analyses of deep rock masses, and implementing timely and precise roadway support measures. Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations. This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals. Based on conventional convolutional neural networks, the proposed model further incorporates long short-term memory (LSTM) networks and attention mechanisms. The LSTM component eff ectively captures temporal correlations in time-series mining vibration data, while the attention mechanism enhances the models’ ability to focus on critical features within the data. To validate the effectiveness of our proposed model, a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training, validation, and testing purposes. Results demonstrate that the proposed artificial intelligence-based classification method achieves a higher recognition accuracy of 92.21%, significantly outperforming traditional manual classification methods. The proposed model represents a significant advancement in ground pressure monitoring and disaster mitigation.
2025, Vol. 22 
