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<p>Rockburst is a significant safety threat in coal mining, influenced by complex nonlinear dynamic characteristics and multi-factor coupling. This study proposes a rockburst risk prediction method based on the SSA-CNN-MoLSTM-Attention model. The model integrates the local feature extraction capability of convolutional neural networks (CNN), the temporal modeling advantages of the modified long short-term memory network (MoLSTM), and the enhanced feature recognition capability of the attention mechanism. Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model’s performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. The model outperforms traditional methods in mean absolute error (MAE) and root mean square error (RMSE), providing effective insights and a reference for rockburst risk assessment and disaster prevention in mining operations.</p>

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Research on rock burst prediction based on an integrated model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

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  • Junming Zhang,
  • Qiyuan Xia,
  • Hai Wu,
  • Sailei Wei,
  • Zhen Hu,
  • Bing Du,
  • Yuejing Yang,
  • Huaixing Xiong

摘要

Rockburst is a significant safety threat in coal mining, influenced by complex nonlinear dynamic characteristics and multi-factor coupling. This study proposes a rockburst risk prediction method based on the SSA-CNN-MoLSTM-Attention model. The model integrates the local feature extraction capability of convolutional neural networks (CNN), the temporal modeling advantages of the modified long short-term memory network (MoLSTM), and the enhanced feature recognition capability of the attention mechanism. Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model’s performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. The model outperforms traditional methods in mean absolute error (MAE) and root mean square error (RMSE), providing effective insights and a reference for rockburst risk assessment and disaster prevention in mining operations.