Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network

Abstract

Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering. This study developed a “surface + underground” microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-Level Radioactive Waste Geological Disposal Laboratory in China. The data sets of four typical one-dimensional time-domain microseismic signals of rock fracture, blasting, TBM tunneling and drilling are constructed, and the BO-CNN-LSTM model is model was developed to identify and classify these signals. Based on the classification results, the typical time-frequency domain characteristics of the four types of signals are analyzed. The classification results of BO-CNN-LSTM, CNN, LSTM and CNN-LSTM model show that the recognition accuracy of the four models is 98.2 %, 86.7 %, 67.7 % and 92 % respectively. Among all types, the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals. The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features, demonstrating superior performance and stability in the classification task. Finally, the study suggests several future research directions, particularly in the areas of raw signal denoising, and the automation and interpretability of feature extraction.

Keywords

deep ground engineering / microseismic monitoring / signal classification / neural network / Bayesian optimization / CNN-LSTM

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