A real-time evaluation method of unstable rock risk level based on Microseismic data and CWT-CNN integrated algorithm

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Geological hazards caused by unstable rocks, including rock collapse and fall, pose significant threats to both engineering productivity and the safety of residents, resulting in substantial economic losses. This study proposed a comprehensive CNN (Convolutional Neural Networks) classification recognition algorithm based on continuous wavelet analysis of microseismic monitoring data, which establishes a CNN recognition-based method for identifying risk levels of unstable rocks to predict their real-time collapse. To collect training data, fixed-base tests were conducted, and validation was performed using vibration data from freeze-thaw tests. Results of the study showed that the accuracy of the CNN classification recognition model trained with fixed-base test data reached 97.6%, and the per-second classification accuracy of vibration segments from freeze-thaw tests was above 86%. Furthermore, the study discussed the correlation between the calculated risk-level eigenvalues and safety coefficients. The CNN risk-level eigenvalues were found to be highly negatively correlated with the safety coefficients, with correlation coefficients as high as 0.8703. Finally, the study verified the superiority of the precursor identification of the risk-level evaluation method by comparing it with the safety coefficients.

Keywords

unstable rock / continuous wavelet transform / convolutional neural networks / risk assessment

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] Vick L, Bohme M, Rouyet L, et al. (2020) Structurally controlled rock slope deformation in northern Norway. Landslides 17: 1745-1776. https://doi.org/10.1007/s10346-020-01421-7

[2] Bolla A, Paronuzzi P (2020) Geomechanical Field Survey to Identify an Unstable Rock Slope: The Passo della Morte Case History (NE Italy). Rock Mech Rock Eng 53: 1521-1544. https://doi.org/10.1007/s00603-019-01963-w

[3] Bolla A, Paronuzzi P (2020) Numerical Investigation of the Pre-collapse Behavior and Internal Damage of an Unstable Rock Slope. Rock Mech Rock Eng 53: 2279-2300. https://doi.org/10.1007/s00603-019-02031-z

[4] Du Y, Xie MW, Jiang Y, et al. (2015) Methods for determining early warning indices Based on natural frequency monitoring. Rock Soil Mech 36: 2284-2290. https://doi.org/10.16285/j.rsm.2015.08.022L

[5] Du Y, Xie MW, Jiang Y, et al. (2019) Research progress on dynamic monitoring index for early warning of rock collapse. Chin J Eng 41: 427-435. https://doi.org/10.13374/j.issn2095-9389.2019.04.002

[6] Ma G. (2012) Study on Evaluating Rock Block Stability by Using A Remotely Positioned Laser Doppler Vibrometer. Int J Geomate 2: 247-252. https://doi.org/10.21660/2012.4.3j

[7] Ma G, Sawada K, Yashima A, et al. (2014) Experimental Study of the Applicability of the Remotely Positioned Laser Doppler Vibrometer to Rock-Block Stability Assessment. Rock Mech Rock Eng 48: 787-802. https://doi.org/10.1007/s00603-014-0577-x

[8] Du Y, Xie MW (2022) Indirect method for the quantitative identification of unstable rock. Nat Hazards 112: 1005-1012. https://doi.org/10.1007/s11069-021-05197-4

[9] Du Y, Wu Z, Xie MW, et al. (2018) Early-warning method of rock collapse and its experimental verification. J China Coal Soc 44: 3069-3075. https://doi.org/10.13225/j.cnki.jccs.2018.1467

[10] Du Y, Xie MW, Jiang YJ, et al. (2017) Experimental Rock Stability Assessment Using the Frozen-Thawing Test. Rock Mech Rock Eng 50: 1049-1053. https://doi.org/10.1007/s00603-016-1138-2

[11] Jia YC, Song GH, Jiang T et al. (2022) Buried depth calculation of the slope of the unstable rock based on natural vibration frequency. Front Earth Sci 10. https://doi.org/10.3389/feart.2022.929825

[12] Jia B, Wu Z, Du Y (2019) Real-time stability assessment of unstable rocks based on fundamental natural frequency. Int J Rock Mech Min Sci 124: 104-134. https://doi.org/10.1016/j.ijrmms.2019.104134

[13] Cao JH, Wei ZB (2011) Denoising in Steel Structural Impulsive Vibration Signal Based on Independent Component Analysis. Adv Mat Res 219-220: 1337-1341. https://doi.org/10.4028/www.scientific.net/AMR.219-220.1337

[14] Fan G, Li J, Hao H. (2020) Vibration signal denoising for structural health monitoring by residual convolutional neural networks. Measurement 157. https://doi.org/10.1016/j.measurement.2020.107651

[15] Huo L, Du Y, Xie M, et al. (2021) Unstable rock mass identification method based on multi-level dynamic parameters. Chin J Rock Mech Eng 41: 3124-3131. https://doi.org/10.13722/j.cnki.jrme.2021.1058

[16] Huo L, Du Y, Xie M, et al. (2021) Chinese Journal of Rock Mechanics and Engineering. Chin J Rock Mech Eng 40: 3156-3162. https://doi.org/10.13722/j.cnki.jrme.2021.0337

[17] Jooshaki M, Nad A, Michaux S . (2021) A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry. Minerals 11: https://doi.org/10.3390/min11080816

[18] Kang J, Ullah Z, Gwak J (2021) MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers. Sensors 21: https://doi.org/10.3390/s21062222

[19] Liao YY, Han L, Wang HY, et al. (2022) Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review. Sensors https://doi.org/10.3390/s22197275

[20] Cheng HD, Chen HX, Li ZF, et al. (2020) Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition. Energy Build 224: 110256. https://doi.org/10.1016/j.enbuild.2020.110256

[21] Oh BK, Park Y, Park HS (2022) Seismic response prediction method for building structures using convolutional neural network. Struct Control Hlth 27: https://doi.org/10.1002/stc.2519

[22] Hu XD, Zhang PL, Zhang Q, et al. (2020) A novel framework of cnn integrated with adaboost for remote sensing scene classification. IGARSS 2020 - 2020 Ieee International Geoscience and Remote Sensing Symposium 2643-2646. https://doi.org/10.1109/IGARSS39084.2020.9324261.

[23] Zhu HH, Azarafza M, Akgun H (2022) Deep learning-based key-block classification framework for discontinuous rock slopes. J Rock Mech Geotech Eng 14: 1131-1139. https://doi.org/10.1016/j.jrmge.2022.06.007

[24] Zhang H, Ma C, Pazzi V, et al. (2020) Deep Convolutional Neural Network for Microseismic Signal Detection and Classification. Pure Appl Geophys 177: 5781-5797. https://doi.org/10.1007/s00024-020-02617-7

[25] Zhang T, Shi D, Wang Z, et al. (2022) Vibration-based structural damage detection via phase-based motion estimation using convolutional neural networks. Mech Syst Signal Process 178: 109320. https://doi.org/10.1016/j.ymssp.2022.109320

[26] Yin X, Liu Q, Huang X, et al. (2021) Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application. Tunn Undergr Space Technol 117: 104133. https://doi.org/10.1016/j.tust.2021.104133

[27] LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553): 436-444. doi: 10.1038/nature14539. PMID: 26017442.

[28] Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton . (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM 60: 84-90.

[29] Du Y, Huo L, Zhang H, et al. (2022) Experimental research on remote monitoring and early warning of rock collapse. J China Univ Min Technol 51: 1201-1208. https://doi.org/10.13247/j.cnki.jcumt.001427

[30] Zhang X, Xie M, Zhang L, et al. (2022) Study on calculation model of stability coefficient of falling dangerous rock mass based on natural frequency. Chin J Rock Mech Eng 1-9. https://doi.org/10.13722/j.cnki.jrme.2022.0361

[31] Zhang WG, Li HR, Li YQ, et al. (2021) Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 54: 5633-5673. https://doi.org/10.1007/s10462-021-09967-1

[32] Lin H, Kumar Singh S, Xiang Z, et al. (2022) An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout. Int J Min Sci Technol 32: 1021-1029. https://doi.org/10.1016/j.ijmst.2022.06.005

[33] Bai F, Fan M, Yang H, et al. (2021) Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources. Int J Min Sci Technol 31: 1053-1061. https://doi.org/10.1016/j.ijmst.2021.09.004

[34] Zhang Y, Tang J, Cheng Y, et al. (2022) Prediction of landslide displacement with dynamic features using intelligent approaches. Int J Min Sci Technol 32: 539-549. https://doi.org/10.1016/j.ijmst.2022.02.004

[35] Jong SC, Ong DEL, Oh E . (2021) State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction. Tunn Undergr Sp Tech 113. https://doi.org/10.1016/j.tust.2021.103946

[36] Zhong T, Cheng M, Lu SP, et al. (2022) RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records. IEEE Geosci Remote Sens Lett 19: 1-5. https://doi.org/10.1109/LGRS.2021.3127637

[37] Tang HH, Cheng SJ, Li WQ, et al. (2022) Simultaneous reconstruction and denoising for DAS-VSP seismic data by RRU-net. Front Earth Sci 10. https://doi.org/10.3389/feart.2022.993465

[38] Sadeqi A, Moradi S . (2021) A new SVD-based filtering technique for operational modal analysis in the presence of harmonic excitation and noise. J Sound Vib 510. https://doi.org/10.1016/j.jsv.2021.116252

[39] Temeng VA, Ziggah YY, Arthur CK . (2020) A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network. Int J Min Sci Technol 30: 683-689. https://doi.org/10.1016/j.ijmst.2020.05.020

[40] Soofastaei A, Aminossadati SM, Arefi MM, et al. (2016) Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption. Int J Min Sci Technol 26: 285-293. https://doi.org/10.1016/j.ijmst.2015.12.015

[41] Luo Z, Zuo H, Jia N, et al. (2013) Instability identification on large scale underground mined-out area in the metal mine based on the improved FRBFNN. Int J Min Sci Technol 23: 821-826. https://doi.org/10.1016/j.ijmst.2013.10.007

[42] Shrestha A, Mahmood A . (2020) Review of Deep Learning Algorithms and Architectures. IEEE Access 7: 53040-53065. 10.1109/ACCESS.2019.2912200

[43] Monga V, Li YL, Eldar YC . (2020) Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing. IEEE Signal Process Mag 38: 18-44. https://doi.org/10.1109/MSP.2020.3016905