Review of machine learning and deep learning application in mine microseismic event classification
Wang Jinqiang1, Prabhat Basnet1, Shakil Mahtab2
1School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, 10083, China
2Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China
Min. miner. depos. 2021, 15(1):19-26
Full text (PDF)
Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others.
Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed.
Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events.
Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn.
Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst.
Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning
- Pu, Y., Apel, D.B., Liu, V., & Mitri, H. (2019). Machine learning methods for rockburst prediction-state-of-the-art review. International Journal of Mining Science and Technology, 29(4), 565-570.https://doi.org/10.1016/j.ijmst.2019.06.009
- Driad-Lebeau, L., Lokmane, N., Semblat, J.F., & Bonnet, G. (2009). Local amplification of deep mining induced vibrations Part 1: Experimental evidence for site effects in a coal basin. Soil Dynamics and Earthquake Engineering, 29(1), 39-50. https://doi.org/10.1016/j.soildyn.2008.01.014
- Malovichko, D. (2012). Discrimination of blasts in mine seismology. Proceedings of the Sixth International Seminar on Deep and High Stress Mining. https://doi.org/10.36487/acg_rep/1201_11_malovichko
- Ge, M. (2005). Efficient mine microseismic monitoring. International Journal of Coal Geology, 64(1-2), 44-56. https://doi.org/10.1016/j.coal.2005.03.004
- Mousavi, S., & Langston, C. (2016). Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding. Bulletin of the Seismological Society of America, 106(4), 1380-1393. https://doi.org/10.1785/0120150345
- Zhao, Z., & Gross, L. (2017). Using supervised machine learning to distinguish microseismic from noise events. SEG Technical Program Expanded Abstracts 2017.https://doi.org/10.1190/segam2017-17727697.1
- Dong, L., Li, X., & Xie, G. (2014). Nonlinear methodologies for identifying seismic event and nuclear explosion using random forest, support vector machine, and naive bayes classification. Abstract and Applied Analysis, (2014), 1-8.https://doi.org/10.1155/2014/459137
- Langer, H., Falsaperla, S., Powell, T., & Thompson, G. (2006). Automatic classification and a-posteriori analysis of seismic event identification at Soufrière Hills volcano, Montserrat. Journal of Volcanology and Geothermal Research, 153(1-2), 1-10. https://doi.org/10.1016/j.jvolgeores.2005.08.012
- Arrowsmith, S., Arrowsmith, M., Hedlin, M., & Stump, B. (2006). Discrimination of mining events using regional seismic and infrasound waveforms: Application to the US and Russia. 28th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies, 549-559.
- Yaghmaei-Sabegh, S. (2017). A novel approach for classification of earthquake ground-motion records. Journal of Seismology, 21(4), 885-907.https://doi.org/10.1007/s10950-017-9642-8
- Li, N., Huang, B., Zhang, X., Yuyang, T., & Li, B. (2019). Characteristics of microseismic waveforms induced by hydraulic fracturing in coal seam for coal rock dynamic disasters prevention. Safety Science, (115), 188-198. https://doi.org/10.1016/j.ssci.2019.01.024
- Yu, Z., He, X., & Zhu, Q. (2014). The wavelet fractal characteristic of micro-seismic waveinmining. Journal of Safety Science and Technology, 10(06), 27-32.
- Jiang, F., Yin, Y., & Zhu, Q. (2014). Feature extraction and classification of mining microseismic waveforms via multi-channels analysis. Journal of China Coal Society, 39(2), 229-237.
- Tan, J., Stewart, R., & Wong, J. (2009). Classification of microseismic events via principal component analysis of trace statistics. Recorder, 35(01), 1-13.
- Dargahi-Noubary, G. (1998). Identification of seismic events based on stochastic properties of the short-period records. Soil Dynamics and Earthquake Engineering, 17(2), 101-115. https://doi.org/10.1016/s0267-7261(97)00026-2
- Arrowsmith, S.J., Arrowsmith, M.D., Hedlin, M.A.H., & Stump, B. (2006). Discrimination of delay-fired mine blasts in wyoming using an automatic time-frequency discriminant. Bulletin of the Seismological Society of America, 96(6), 2368-2382. https://doi.org/10.1785/0120060039
- Wilkins, A.H., Strange, A., Duan, Y., & Luo, X. (2020). Identifying microseismic events in a mining scenario using a convolutional neural network. Computers & Geosciences, (137), 104418. https://doi.org/10.1016/j.cageo.2020.104418
- Musil, M., & Pleginger, A. (1996). Discrimination between local microearthquakes and quarry blasts by multi-layer Perceptrons and Kohonen maps. Bulletin of The Seismological Society of America, (86), 1077-1090.
- Scarpetta, S., Giudicepietro, F., Ezin, E., Petrosino, S., Del Pezzo, E., Martini, M., & Marinaro, M. (2005). Automatic classification of seismic signals at Mt. Vesuvius Volcano, Italy, using neural networks. Bulletin of the Seismological Society of America, 95(1), 185-196. https://doi.org/10.1785/0120030075
- Jingrong Zhao, Ke Wang, & Yang Guo. (2010). Acoustic emission signals classification based on support vector machine. 2010 2nd International Conference on Computer Engineering and Technology. https://doi.org/10.1109/iccet.2010.5486240
- Ruano, A., Madureira, G., Barros, O., Khosravani, H., Ruano, M., & Ferreira, P. (2014). Seismic detection using support vector machines. Neurocomputing, (135), 273-283. https://doi.org/10.1016/j.neucom.2013.12.020
- Provost, F., Hibert, C., & Malet, J. (2017). Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier. Geophysical Research Letters, 44(1), 113-120. https://doi.org/10.1002/2016gl070709
- Ambuter, B., & Solomon, S. (1974). An event-recording system for monitoring small earthquakes. Bulletin of the Seismological Society of America, 64(4), 1181-1188.
- Munro, K. (2004). Automatic event detection and picking of P-wave arrivals. CREWES Research Report, 1-10.
- Cao, A., Dou, L., Yan, R., Jiang, H., Lu, C., Du, T., & Lu, Z. (2009). Classification of microseismic events in high stress zone. Mining Science and Technology (China), 19(6), 718-723. https://doi.org/10.1016/s1674-5264(09)60131-9
- Zhao, G., Ma, J., Dong, L., Li, X., Chen, G., & Zhang, C. (2014). Classification of mine blasts and microseismic events using starting-up features in seismograms. Transactions of Nonferrous Metals Society of China, 25(10), 3410-3420. https://doi.org/10.1016/S1003-6326(15)63976-0
- Li, X., Li, Z., Wang, E., Feng, J., Kong, X., Chen, L., & Li, N. (2016). Analysis of natural mineral earthquake and blast based on Hilbert-Huang transform (HHT). Journal of Applied Geophysics, (128), 79-86. https://doi.org/10.1016/j.jappgeo.2016.03.024
- Pan, Y., Feng, L., & Li, Z. (2011). The model of energy-absorbing coupling support and its application in rock burst roadway. Journal of Mining and Safety Engineering, 28(1), 6-10.
- Li, X., Zhang, Y., & Liu, Z. (2005). Wavelet analysis and Hilbert-Huang transform of blasting vibration signal. Explosion and Shock Waves, 25(6), 528-535. https://doi.org/10.1360/biodiv.050084
- Vaezi, Y., & Van der Baan, M. (2015). Comparison of the STA/LTA and power spectral density methods for microseismic event detection. Geophysical Journal International, 203(3), 1896-1908. https://doi.org/10.1093/gji/ggv419
- Peng, W., Xu, C., Yi-Bo, W., Lu-Chen, W., & Hong-Yu, Z. (2014). Automatic event detection and event recovery in low SNR microseismic signals based on time-frequency sparseness. Chinese Journal of Geophysics, 57(5), 739-749. https://doi.org/10.1002/cjg2.20137
- Tselentis, G., Martakis, N., Paraskevopoulos, P., Lois, A., & Sokos, E. (2011). A method for microseismic event detection and P‐phase picking. SEG Technical Program Expanded Abstracts 2011. https://doi.org/10.1190/1.3627517
- Zhang, P., Jiang, X., & Yang, S. (2005). The differences of wave spectrum among explosion, mine tremor and earthquake. Seismological and Geomagnetic Observation and Research.
- Hu, N., Du, F., & Li, G. (2016). Discriminator of mining blasts and microseismic events based on multi-scale discrete wavelet transform. Electronic Journal of Geotechnical Engineering, 21(03), 1267-1278.
- McCulloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/bf02478259
- Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. https://doi.org/10.1037/h0042519
- Block, H.D. (1970). A review of “perceptrons: An introduction to computational geometry≓. Information and Control, 17(5), 501-522. https://doi.org/10.1016/s0019-9958(70)90409-2
- Hubel, D., & Wiesel, T. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106-154. https://doi.org/10.1113/jphysiol.1962.sp006837
- Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1985). Learning internal representations by error propagation. Virginia, United States: Defense Technical Information Center. https://doi.org/10.21236/ada164453
- LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., & Jackel, L.D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541-551. https://doi.org/10.1162/neco.19126.96.36.1991
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/bf00994018
- Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. ICML’96: Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, 148-156.
- Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J., & Rajabi, M. (2014). Advantage and drawback of support vector machine functionality. 2014 International Conference on Computer, Communications, and Control Technology (I4CT). https://doi.org/10.1109/i4ct.2014.6914146
- Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, (61), 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.19188.8.131.525
- Hinton, G.E. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. https://doi.org/10.1126/science.1127647
- Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann machines. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, (5), 448-455.
- Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
- Goodfellow, I., Abadie, J., Mirza, M., Xu, B., Farley, D., & Ozair, S. (2014). Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems, 2672-2680.
- Budakoğlu, E., & Horasan, G. (2018). Classification of seismic events using linear discriminant function (LDF) in the Sakarya region, Turkey. Acta Geophysica, 66(5), 895-906. https://doi.org/10.1007/s11600-018-0179-1
- Ma, J., Zhao, G., Dong, L., Chen, G., & Zhang, C. (2015). A comparison of mine seismic discriminators based on features of source parameters to waveform characteristics. Shock and Vibration, (2015), 1-10. https://doi.org/10.1155/2015/919143
- Peng, P., He, Z., Wang, L., & Jiang, Y. (2020). Automatic classification of microseismic records in underground mining: A deep learning approach. IEEE Access, (8), 17863-17876. https://doi.org/10.1109/access.2020.2967121
- Zhu, Q., Jiang, F., Ming, Y., Xing, Y., & Lin, W. (2012). Classification of mine microseismic events based on wavelet-fractal method and pattern recognition. Chinese Journal of Geotechnical Engineering, 34(11), 2036-2042. https://doi.org/10.1007/s11783-011-0280-z
- Shang, X., Li, X., Peng, K., & Dong, L. (2016). Feature extraction and classification of mine microseism and blast based on EMD-SVD. Journal of Geotechnical Engineering, (38), 1849-1858.
- Shang, X., Li, X., & Peng, K. (2017). Application of FSWT-SVD model in the feature extraction of rock mass microseismic signals. Journal of Vibration and Shock, 36(14), 52-60. https://doi.org/10.13465/j.cnki.jvs.2017.14.008
- Li, W. (2017). Feature extraction and classification method of mine microseismic signals based on lmd and pattern recognition. Journal of China Coal Society, (42), 1156-1164.
- Jia, R., Sun, H., Peng, Y., Liang, Y., & Lu, X. (2016). Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine. Journal of Seismology, 21(4), 735-748. https://doi.org/10.1007/s10950-016-9632-2
- Lin, B., Wei, X., Junjie, Z., & Hui, Z. (2018). Automatic classification of multi-channel microseismic waveform based on DCNN-SPP. Journal of Applied Geophysics, (159), 446-452. https://doi.org/10.1016/j.jappgeo.2018.09.022
- Lin, B., Wei, X., & Junjie, Z. (2019). Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM. Computers & Geosciences, (123), 111-120. https://doi.org/10.1016/j.cageo.2018.10.008
- Peng, P., He, Z., & Wang, L. (2019). Automatic classification of microseismic signals based on MFCC and GMM-HMM in underground mines. Shock and Vibration, (2019), 1-9. https://doi.org/10.1155/2019/5803184
- Chen, Y., Zhang, G., Bai, M., Zu, S., Guan, Z., & Zhang, M. (2019). Automatic waveform classification and arrival picking based on convolutional neural network. Earth and Space Science, 6(7), 1244-1261. https://doi.org/10.1029/2018ea000466
- Choi, W.C., Kim, C., Cheon, D., & Pyun, S. (2019). Automatic classification of microseismic signals related to mining activities by supervised learning. 81st EAGE Conference and Exhibition 2019. https://doi.org/10.3997/2214-4609.201900757
- Song, G., Cheng, J., & Grattan, K.T.V. (2020). Recognition of microseismic and blasting signals in mines based on convolutional neural network and stockwell transform. IEEE Access, (8), 45523-45530. https://doi.org/10.1109/access.2020.2978392
- Vallejos, J.A., & McKinnon, S.D. (2013). Logistic regression and neural network classification of seismic records. International Journal of Rock Mechanics and Mining Sciences, (62), 86-95. https://doi.org/10.1016/j.ijrmms.2013.04.005
- Dong, L., Wesseloo, J., Potvin, Y., & Li, X. (2015). Discrimination of mine seismic events and blasts using the fisher classifier, naive Bayesian classifier and logistic regression. Rock Mechanics and Rock Engineering, 49(1), 183-211. https://doi.org/10.1007/s00603-015-0733-y
- Dong, L.-J., Wesseloo, J., Potvin, Y., & Li, X.-B. (2016). Discriminant models of blasts and seismic events in mine seismology. International Journal of Rock Mechanics and Mining Sciences, (86), 282-291. https://doi.org/10.1016/j.ijrmms.2016.04.021
- Shang, X., Li, X., Morales-Esteban, A., & Chen, G. (2017). Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis. Soil Dynamics and Earthquake Engineering, (99), 142-149. https://doi.org/10.1016/j.soildyn.2017.05.008
- Zhou, Z., Cheng, R., Cai, X., Ma, D., & Jiang, C. (2018). Discrimination of rock fracture and blast events based on signal complexity and machine learning. Shock and Vibration, (2018), 1-10. https://doi.org/10.1155/2018/9753028
- Pu, Y., Apel, D.B., & Hall, R. (2020). Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models. Engineering Geology, (268), 105519. https://doi.org/10.1016/j.enggeo.2020.105519