Recognition and classification of microseismic signals based on a 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

References

[1] Liu J-p, Si Y-t, Wei D-cet al. . Developments and prospects of microseismic monitoring technology in underground metal mines in China [J]. Journal of Central South University. 2021, 28(10): 3074-3098.

[2] Cheng B-r, Feng X-t, Fu Q-qet al. . Integration and high precision intelligence microseismic monitoring technology and its application in deep rock engineering [J]. Rock and Soil Mechanics. 2020, 41(7): 2422-2431

[3] Dong X-t, Lin J, Lu S-pet al. . Seismic shot gather denoising by using a supervised-deep-learning method with weak dependence on real noise data: A solution to the lack of real noise data [J]. Surveys in Geophysics. 2022, 43(5): 1363-1394.

[4] Li J-m, Tang S-b, Li K-yet al. . Automatic recognition and classification of microseismic waveforms based on computer vision [J]. Tunnelling and Underground Space Technology. 2022, 121: 104327.

[5] Barthwal H, Shcherbakov R. Unsupervised clustering of mining-induced microseismicity provides insights into source mechanisms [J]. International Journal of Rock Mechanics and Mining Sciences. 2024, 183105905.

[6] Velis D, Sabbione J I, Sacchi M D. Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering [J]. Geophysics. 2015, 80(6): WC25-WC38.

[7] Zuo L-q, Sun H-m, Mao Q-cet al. . Noise suppression method of microseismic signal based on complementary ensemble empirical mode decomposition and wavelet packet threshold [J]. IEEE Access. 2019, 7: 176504-176513.

[8] Ma J, Zhao G-y, Dong L-jet al. . A comparison of mine seismic discriminators based on features of source parameters to waveform characteristics [J]. Shock and Vibration. 2015, 20151919143

[9] Dong L-j, Wesseloo J, Potvin Yet al. . Discrimination of mine seismic events and blasts using the fisher classifier, naive Bayesian classifier and logistic regression [J]. Rock Mechanics and Rock Engineering. 2016, 491183-211.

[10] Abdalzaher M S, Soliman M S, El-Hady S Met al. . A deep learning model for earthquake parameters observation in IoT system-based earthquake early warning [J]. IEEE Internet of Things Journal. 2022, 9(11): 8412-8424.

[11] Pu Y-y, Apel D B, Hall R. Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models [J]. Engineering Geology. 2020, 268: 105519.

[12] Zhao G-y, Ma J, Dong L-jet al. . Classification of mine blasts and microseismic events using starting-up features in seismograms [J]. Transactions of Nonferrous Metals Society of China. 2015, 25(10): 3410-3420.

[13] Dong L-j, Sun D-y, Li X-bet al. . A statistical method to identify blasts and microseismic events and its engineering application [J]. Chinese Journal of Rock Mechanics and Engineering. 2016, 3571423-1433(in Chinese)

[14] Kinali M, Pytharouli S, Lunn R Jet al. . Detection of weak seismic signals in noisy environments from unfiltered, continuous passive seismic recordings [J]. Bulletin of the Seismological Society of America. 2018, 108(5B): 2993-3004.

[15] Wei H, Shu W-w, Dong L-jet al. . A waveform image method for discriminating micro-seismic events and blasts in underground mines [J]. Sensors. 2020, 20(15): 4322.

[16] Liu H, Zhang J-zhong. STA/LTA algorithm analysis and improvement of Microseismic signal automatic detection [J]. Progress in Geophysics. 2014, 2941708-1714(in Chinese)

[17] Liang Z-z, Xue R-x, Xu N-wet al. . Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring [J]. Tunnelling and Underground Space Technology. 2020, 105: 103564.

[18] Dong L-j, Tang Z, Li X-bet al. . Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform [J]. Journal of Central South University. 2020, 27(10): 3078-3089.

[19] Fan X, Cheng J-y, Wang Y-het al. . Automatic events recognition in low SNR microseismic signals of coal mine based on wavelet scattering transform and SVM [J]. Energies. 2022, 1572326.

[20] Qu S, Guan Z, Verschuur Eet al. . Automatic high-resolution microseismic event detection via supervised machine learning [J]. Geophysical Journal International. 2020, 22211881-1895.

[21] Peng P-g, Lei R, Wang J-miao. Enhancing microseismic signal classification in metal mines using transformer-based deep learning [J]. Sustainability. 2023, 15(20): 14959.

[22] Li J-m, Tang S-b, Weng F-wet al. . Waveform recognition and process interpretation of microseismic monitoring based on an improved LeNet5 convolutional neural network [J]. Journal of Central South University. 2023, 303904-918.

[23] Zhang X-l, Wang X-h, Zhang Z-het al. . CNN-transformer for microseismic signal classification [J]. Electronics. 2023, 12(11): 2468.

[24] Zhang X-l, Zhang Z-h, Jia R-set al. . Research on microseismic signal identification through data fusion [J]. Computers & Geosciences. 2024, 192: 105708.

[25] Wang Y-j, Qiu Q, Lan Z-qet al. . Identifying microseismic events using a dual-channel CNN with wavelet packets decomposition coefficients [J]. Computers & Geosciences. 2022, 166105164.

[26] Tian J-h, Tian Z-c, Zhang M-wet al. . A novel identification method of microseismic events based on empirical mode decomposition and artificial neural network features [J]. Journal of Applied Geophysics. 2024, 222105329.

[27] Shu L-y, Liu Z-s, Wang Ket al. . Characteristics and classification of microseismic signals in heading face of coal mine: Implication for coal and gas outburst warning [J]. Rock Mechanics and Rock Engineering. 2022, 55116905-6919.

[28] Dao F, Zeng Y, Qian J. Fault diagnosis of hydro-turbine via the incorporation of Bayesian algorithm optimized CNN-LSTM neural network [J]. Energy. 2024, 290: 130326.

[29] Liu S-h, Wu Y-d, Huang R. Prediction of drilling plug operation parameters based on incremental learning and CNN-LSTM [J]. Geoenergy Science and Engineering. 2024, 234212631.

[30] Zha W-s, Liu Y-p, Wan Y-jet al. . Forecasting monthly gas field production based on the CNN-LSTM model [J]. Energy. 2022, 260C124889.

[31] Wang H-j, Zhang L-m, Luo H-yet al. . AI-powered landslide susceptibility assessment in Hong Kong [J]. Engineering Geology. 2021, 288106103.

[32] Liu J-f, He X, Huang H-yet al. . Predicting gas flow rate in fractured shale reservoirs using discrete fracture model and GA-BP neural network method [J]. Engineering Analysis with Boundary Elements. 2024, 159315-330.

[33] Wang Y-k, Tang H-m, Huang J-set al. . A comparative study of different machine learning methods for reservoir landslide displacement prediction [J]. Engineering Geology. 2022, 298: 106544.

[34] Abbaszadeh M, Soltani-Mohammadi S, Ahmed A N. Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method [J]. Computers & Geosciences. 2022, 165105140.

[35] Gao L-r, Yan H-b, Liu T-zet al. . Prediction of environmental parameters of Yungang Grottoes based on BO-CNN-LSTM artificial neural network [C]. Advanced Control and Intelligent Computing Applications. 2025, Singapore, Springer159-173.

[36] Yang Z-b, Yuan H-p, Cai Xet al. . Monitoring technology of hydroturbines in pumped storage power stations: A mini review [J]. Frontiers in Energy Research. 2024, 12: 1478072.

[37] Wang X, Pan Y, Chen J-jet al. . A spatiotemporal feature fusion-based deep learning framework for synchronous prediction of excavation stability [J]. Tunnelling and Underground Space Technology. 2024, 147105733.

[38] Cai J-j, Wang L-n, Wen Z-het al. . Robust fault diagnosis for drilling machinery in challenging environments [C]. 2024 IEEE International Conference on Smart Internet of Things (SmartIoT). November 14–16, 2024, Shenzhen, China. 2024502-507

[39] Dai M-l, Huang Z-qiang. Research on fault diagnosis of drilling pump fluid end based on time-frequency analysis and convolutional neural network [J]. Processes. 2024, 1291929.

[40] Lecun Y, Bottou L, Bengio Yet al. . Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE. 1998, 86112278-2324.

[41] Yu Y, Si X-s, Hu C-het al. . A review of recurrent neural networks: LSTM cells and network architectures [J]. Neural Computation. 2019, 31(7): 1235-1270.

[42] Li G-n, Zhao X-w, Fan Cet al. . Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions [J]. Journal of Building Engineering. 2021, 43: 103182.

[43] Zhou J, Shen X-j, Qiu Y-get al. . Microseismic location in hardrock metal mines by machine learning models based on hyperparameter optimization using Bayesian optimizer [J]. Rock Mechanics and Rock Engineering. 2023, 56(12): 8771-8788.

[44] Wang J, Chen L, Su Ret al. . The Beishan underground research laboratory for geological disposal of high-level radioactive waste in China: Planning, site selection, site characterization and in situ tests [J]. Journal of Rock Mechanics and Geotechnical Engineering. 2018, 103411-435.

RIGHTS & PERMISSIONS

Central South University