Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application

• Published in: Tunnelling and underground space technology Vol. 117; p. 104133
• Main Authors: Yin, Xin, Liu, Quansheng, Huang, Xing, Pan, Yucong
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-11-2021; Elsevier BV
• Subjects: Adaptive moment estimation optimization; Algorithms; Artificial neural networks; Back propagation networks; Bayes optimization; Continuous wavelet transform; Convolutional neural network; Excavation; Geology; Kernels; Machine learning; Mathematical models; Microseismic data; Microseisms; Optimization; Oversampling; Parameters; Parametric analysis; Real time; Rockburst prediction; Rockbursts; Support vector machines; Training; Warning systems; Wavelet transforms; Workflow; Adaptive moment estimation optimization; Bayes optimization; Microseismic data; Convolutional neural network; Rockburst prediction
• ISSN: 0886-7798; 1878-4364
• DOI: 10.1016/j.tust.2021.104133

•A novel rockburst prediction framework is raised based on microseismic monitoring and machine learning.•An integrated CNN-Adam-BO algorithm is proposed to recognize the microseismic sequence.•A field rockburst risk warning system is demonstrated.•Compared to BPNN, SVM, and two benchmark models, the CNN-Adam-BO model is the most superior.
Rockburst is a dynamic geological disaster common during underground excavation, which significantly threatens the safety of personnel, equipment, and property. This paper proposes an integrated CNN-Adam-BO algorithm based on microseismic monitoring data to achieve real-time prediction of rockburst intensity. A total of 400 rockburst cases were collected from a water conveyance tunnel in northwestern China, including 100 no rockbursts, 252 low rockbursts, and 48 moderate rockbursts. During modeling, adaptive moment estimation optimization and Bayes optimization were separately used to tune the CNN network hyper-parameters (i.e., convolution kernels and bias) as well as dropout probability, and a parametric analysis was carried out to optimize the CNN structure and training parameters (i.e., the CNN architecture, the number of kernels in the convolutional layer, the number of neurons in the full connection layer, and the number of training epochs). To assess model performance, 2 global metrics (i.e., accuracy and Kappa) and 3 within-class metrics (i.e., precision, recall, and F1-score) were introduced. Additionally, a comparison analysis with other machine learning models, such as a back-propagation neural network, a support vector machine, and two CNN-based benchmark models, was conducted in regard to prediction ability and running speed. Results show that the CNN-Adam-BO model is the most superior, which achieved an accuracy of 91.67%, Kappa of 0.8392, F1-score of 0.8750, 0.9474, and 0.8333 (on no/low/moderate rockburst), and running time of 190.12 s. Taking 2 extra rockburst cases as examples, a complete workflow for how to apply the constructed model to construct a field rockburst risk warning system was illustrated. Further, the effect of data quality, especially data imbalance, on model performance was discussed, indicating that the synthetic minority oversampling technique is an effective way to eliminate data imbalance. Finally, superiority of the dimension enhancement method of the microseismic sequence described in this paper was verified via comparison with the continuous wavelet transform and cross wavelet transform.