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Method for EMR and AE interference signal identification in coal rock mining based on recurrent neural networks

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Abstract

Electromagnetic radiation (EMR) and acoustic emission (AE) are popular geophysical methods for monitoring and providing early warnings about coal rock burst disasters. In the underground mining process, personnel activities and electromechanical equipment produce EMR and AE interference signals that affect the accuracy of EMR and AE monitoring. Currently, these signals are usually identified and filtered based on their time sequence and amplitude characteristics, but the identification and filtering methods need to be further improved. Advancements in the deep learning algorithms provide an opportunity to develop a new identification and filtering method. In this study, a method for identifying EMR and AE interference signals on the basis of deep learning algorithms is proposed. The proposed method, which is based on bidirectional long short-term memory recurrent neural networks and Fourier transform, intelligently identifies and filters EMR and AE signal sequences by analyzing numerous EMR and AE interference signals along with other signals. The results show that the proposed method can respond positively to EMR and AE interferences and accurately eliminate EMR and AE interference signals. It can significantly improve the reliability of EMR and AE monitoring data and effectively monitor rock burst disasters.

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References

  • Baddari K, Frolov AD, Tourtchine V, Rahmoune F (2011) An integrated study of the dynamics of electromagnetic and acoustic regimes during failure of complex macrosystems using rock blocks. Rock Mech Rock Eng 44(3):269–280. Go to ISI://WOS:000290167900002

  • Bahat D, Frid V, Rabinovitch A, Palchik V (2002) Exploration via electromagnetic radiation and fractographic methods of fracture properties induced by compression in glass-ceramic. Int J Fract 116(2):179–194. GotoISI://WOS:000178626000005

  • Carpinteri A, Lacidogna G, Borla O, Manuello A, Niccolini G (2012) Electromagnetic and neutron emissions from brittle rocks failure: Experimental evidence and geological implications. Sadhana-Acad Proc Eng Sci 37(1):59–78. GotoISI://WOS:000302816500005.

  • Das S, Mallik J, Dhankhar S, Suthar N, Singh AK, Dutta V, Gupta U, Kumar G, Singh R (2020) Application of Fracture Induced Electromagnetic Radiation (FEMR) technique to detect landslide-prone slip planes. Nat Hazards 101(2):505–535. Go to ISI://WOS:000516429800002

  • Freund F, Sornette D (2007) Electro-magnetic earthquake bursts and critical rupture of peroxy bond networks in rocks. Tectonophysics 431(1–4):33–47. GotoISI://WOS:000244643300005.

  • Frid V, Vozoff K (2005) Electromagnetic radiation induced by mining rock failure. Int J Coal Geol 64(1–2):57–65. GotoISI://WOS:000232875000005.

  • Frid V, Rabinovitch A, Bahat D (2003) Fracture induced electromagnetic radiation. J Phys D Appl Phys 36(13):1620–1628. GotoISI://WOS:000185360900036.

  • Fukui K, Okubo S, Terashima T (2005) Electromagnetic radiation from rock during uniaxial compression testing: The effects of rock characteristics and test conditions. Rock Mech Rock Eng 38(5):411–423. GotoISI://WOS:000233103600004.

  • Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) A search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232. Go to ISI://WOS:000411293200001

  • He MC, Miao JL, Feng JL (2010) Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions. Int J Rock Mech Min Sci 47(2):286–298. Go to ISI://WOS:000274550200012

  • He KM, Zhang XY, Ren SQ, Sun J (2016) Deep Residual learning for image recognition: 2016 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr), 770–778. https://doi.org/10.1109/Cvpr.2016.90

  • Kombrink S, Mikolov T, Karafiat M, Burget L (2011 Recurrent neural network based language modeling in meeting recognition: 12th Annual Conference of the International Speech Communication Association 2011 (Interspeech 2011), vols 1–5, pp 2888–2891. GotoISI://WOS:000316502201211

  • Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  • Kumar A, Chauhan VS, Sharma SK, Kumar R (2017) Deformation induced electromagnetic response of soft and hard PZT under impact loading. Ferroelectrics 510(1):170–183. https://doi.org/10.1080/00150193.2017.1328726

  • Lacidogna G, Carpinteri A, Manuello A, Durin G, Schiavi A, Niccolini G, Agosto A (2011) Acoustic and electromagnetic emissions as precursor phenomena in failure processes. Strain 47:144–152. https://doi.org/10.1111/j.1475-1305.2010.00750.x

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. GotoISI://WOS:000355286600030

  • Lee L (2000) Foundations of statistical natural language processing. Comput Linguist 26(2):277–279. GotoISI://WOS:000087906500011

  • Li XL, Wang EY, Li ZH, Liu ZT, Song DZ, Qiu LM (2016) Rock burst monitoring by integrated microseismic and electromagnetic radiation methods. Rock Mech Rock Eng 49(11):4393–4406. https://doi.org/10.1007/s00603-016-1037-6

  • Liu XF, Wang EY (2018) Study on characteristics of EMR signals induced from fracture of rock samples and their application in rockburst prediction in copper mine. J Geophys Eng 15(3):909–920. https://doi.org/10.1088/1742-2140/aaa3ce

    Article  Google Scholar 

  • Mansurov VA (2001) Prediction of rockbursts by analysis of induced seismicity data. Int J Rock Mech Min Sci 38(6):893–901. https://doi.org/10.1016/S1365-1609(01)00055-7

  • Mikolov T, Karafiat M, Burget L, Cernocky J, Khudanpur S (2010) Recurrent neural network based language model: 11th Annual Conference of the International Speech Communication Association 2010 (Interspeech 2010), vols 1–2, pp 1045–1048. GotoISI://WOS:000294382400258

  • Palangi H, Deng L, Shen YL, Gao JF, He XD, Chen JS, Song XY, Ward R (2016) Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE-Acm Trans Audio Speech Lang Process 24(4):694–707. https://doi.org/10.1109/Taslp.2016.2520371

  • Qiu LM, Li ZH, Wang EY, Liu ZT, Ou JC, Li XL, Ali M, Zhang YN, Xia SK (2018) Characteristics and precursor information of electromagnetic signals of mining-induced coal and gas outburst. J Loss Prev Process Ind 54:206–215. GotoISI://WOS:000437998000022

    Article  Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  Google Scholar 

  • Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation: IEEE Trans Pattern Anal Mach Intell 39(4):640–651. GotoISI://WOS:000397717600003.

  • Smirnov EA, Timoshenko DM, Andrianov SN (2014) Comparison of regularization methods for ImageNet classification with deep convolutional neural networks: 2nd Aasri Conference on Computational Intelligence and Bioinformatics, 6, pp 89–94. https://doi.org/10.1016/j.aasri.2014.05.013

  • Spichak V, Popova I (2000) Artificial neural network inversion of magnetotelluric data in terms of three-dimensional earth macroparameters. Geophys J Int 142(1):15–26. GotoISI://WOS:000087847800003.

  • Srivastava N, Hinton G, Krizhevsky A, Sutskever L, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. GotoISI://WOS:000344638300002

  • Sun J, Niu Z, Innanen KA, Li JX, Trad DO (2020) A theory-guided deep-learning formulation and optimization of seismic waveform inversion. Geophysics 85(2):R87–R99. GotoISI://WOS:000519538200017

  • Wang EY, Liu XF, He XQ, Ling L (2009) Application of electromagnetic radiation technology in rock burst prediction in coal mines: Controlling seismic hazard and sustainable development of deep mines: 7th International Symposium on Rockburst and Seismicity in Mines (Rasim7), vol 1 and 2, pp 945–950. GotoISI://WOS:000271028900139

  • Wang EY, He XQ, Liu XF, Li ZH, Wang C, Xiao D (2011) A non-contact mine pressure evaluation method by electromagnetic radiation. J Appl Geophys 75(2):338–344. GotoISI://WOS:000296822800020

  • Wrona T, Pan I, Gawthorpe RL, Fossen H (2018) Seismic facies analysis using machine learning. Geophys 83(5):O83-O95. GotoISI://WOS:000453050000031

  • Yang SQ, Jing HW (2011) Strength failure and crack coalescence behavior of brittle sandstone samples containing a single fissure under uniaxial compression. Int J Fract 168(2):227–250. GotoISI://WOS:000287211700008

  • Yu SW, Ma JW, Wang WL (2019) Deep learning for denoising. Geophysics 84(6):V333–V350. https://doi.org/10.1190/Geo2018-0668.1

    Article  Google Scholar 

  • Zhang K, Zuo WM, Chen YJ, Meng DY, Zhang L (2017) Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155. https://doi.org/10.1109/Tip.2017.2662206

    Article  Google Scholar 

  • Zhao Z, Chen WH, Wu XM, Chen PCY, Liu JM (2017) LSTM network: a deep learning approach for short-term traffic forecast. IET Intell Transp Syst 11(2):68–75. https://doi.org/10.1049/iet-its.2016.0208

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51934007, 51574231).

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Authors and Affiliations

  1. Key Laboratory of Gas and Fire Control for Coal Mines (China University of Mining and Technology), Ministry of Education, 221116, Xuzhou, China

    Yangyang Di, Enyuan Wang, Zhonghui Li & Xiaofei Liu

  2. School of Safety Engineering, China University of Mining and Technology, 221116, Xuzhou, Jiangsu, China

    Enyuan Wang, Zhonghui Li, Xiaofei Liu & Baolin Li

  3. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, 221116, Xuzhou, Jiangsu, China

    Enyuan Wang, Zhonghui Li & Xiaofei Liu

Authors
  1. Yangyang Di
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  2. Enyuan Wang
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  3. Zhonghui Li
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  4. Xiaofei Liu
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  5. Baolin Li
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Correspondence to Enyuan Wang.

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Communicated by: H. Babaie

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Yangyang Di collected and analyzed data and wrote this paper.

Enyuan Wang polished and revised the paper.

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Di, Y., Wang, E., Li, Z. et al. Method for EMR and AE interference signal identification in coal rock mining based on recurrent neural networks. Earth Sci Inform 14, 1521–1536 (2021). https://doi.org/10.1007/s12145-021-00658-7

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  • DOI: https://doi.org/10.1007/s12145-021-00658-7

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