Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network

PEI Yan-yu; YANG Xiao-bin; CHUAN Jin-ping; WU Xue-song; CHENG Hong-ming; Lü Xiang-feng

doi:10.13374/j.issn2095-9389.2020.11.22.001

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(7): 1003-1009

PEI Yan-yu, YANG Xiao-bin, CHUAN Jin-ping, WU Xue-song, CHENG Hong-ming, Lü Xiang-feng. Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 1003-1009. doi: 10.13374/j.issn2095-9389.2020.11.22.001

Citation:

PEI Yan-yu, YANG Xiao-bin, CHUAN Jin-ping, WU Xue-song, CHENG Hong-ming, Lü Xiang-feng. Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 1003-1009. doi: 10.13374/j.issn2095-9389.2020.11.22.001

Citation:

PEI Yan-yu, YANG Xiao-bin, CHUAN Jin-ping, WU Xue-song, CHENG Hong-ming, Lü Xiang-feng. Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 1003-1009. doi: 10.13374/j.issn2095-9389.2020.11.22.001

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Time series prediction of microseismic energy level based on feature extraction of one-dimensional convolutional neural network

doi: 10.13374/j.issn2095-9389.2020.11.22.001

1.
School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2.
Yanbei Coal Mine, Huating Coal Industry Company, Huating 744105, China
3.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China

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Corresponding author: E-mail: yangxiaobin02@126.com
Received Date: 2020-11-22
Available Online: 2021-06-02
Publish Date: 2021-07-01

Abstract

Abstract

With the gradual transition of coal mining to deep mining, the number and intensity of rock burst events in the deep mining process are gradually increasing. Thus, it is of great significance to study the change of rock burst precursor signal for the prediction of rock burst. Microseismic signal monitoring plays an important role in rock burst prediction. The microseismic energy level changes with time, a good corresponding relationship exists between the high-energy microseismic events and rock burst. To advance the time node of rock burst prediction and provide more time guarantee for rock burst prevention and control, a time series prediction model of mine microseismic energy based on the one-dimensional convolutional neural network (CNN) was established to predict the temporal variation of mine microseismic energy. Through model training, the energy level of the previous 10 microseismic events can be used as input to predict the energy level of the next microseismic event. Due to the imbalance of the microseismic sample data, the microseismic events of the 10⁶-energy level were all judged as 10⁵-energy level microseismic events in the model test. To improve the prediction accuracy of the model for the 10⁶-energy level microseismic events, a hybrid sampling method was used to train the improved model. Using the microseismic energy level data of 250202 working face in Yanbei coal mine, the overall test accuracy of the improved model reaches 98.4% and the test accuracy of the 10⁶-energy level microseismic events increased to 99%. The improved prediction model of the microseismic energy level time series based on the one-dimensional convolution neural network was applied to 250202 working face of Yanbei coal mine to predict the microseismic energy level time series. The overall prediction accuracy of the model is 93.5%, and the prediction accuracy of high-energy microseismic events is close to 100%.
- time series prediction of microseismic energy levels,
- one-dimensional convolution neural network,
- class imbalance,
- hybrid sampling,
- rock burst

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References(28)

References

[1]	姜耀東, 趙毅鑫. 我國煤礦沖擊地壓的研究現狀: 機制、預警與控制. 巖石力學與工程學報, 2015, 34(11):2188 Jiang Y D, Zhao Y X. State of the art: investigation on mechanism, forecast and control of coal bumps in China. Chin J Rock Mech Eng, 2015, 34(11): 2188
[2]	姜耀東, 趙毅鑫, 劉文崗, 等. 煤巖沖擊失穩的機理和實驗研究. 北京: 科學出版社, 2009 Jiang Y D, Zhao Y X, Liu W G, et al. Investigation on the Mechanism of Coal Bumps and Relating Experiment. Beijing: Science Press, 2009
[3]	苗小虎, 姜福興, 王存文, 等. 微地震監測揭示的礦震誘發沖擊地壓機理研究. 巖土工程學報, 2011, 33(6):971 Miao X H, Jiang F X, Wang C W, et al. Mechanism of microseism-inducdrock burst revealed by microseismic monitoring. Chin J Geotech Eng, 2011, 33(6): 971
[4]	袁瑞甫, 李化敏, 李懷珍. 煤柱型沖擊地壓微震信號分布特征及前兆信息判別. 巖石力學與工程學報, 2012, 31(1):80 doi: 10.3969/j.issn.1000-6915.2012.01.010 Yuan R F, Li H M, Li H Z. Distribution of microseismic signal and discrimination of portentous information of pillar type rockburst. Chin J Rock Mech Eng, 2012, 31(1): 80 doi: 10.3969/j.issn.1000-6915.2012.01.010
[5]	Pytel W, ?witoń J, Wójcik A. The effect of mining face’s direction on the observed seismic activity. Int J Coal Sci Technol, 2016, 3(3): 322 doi: 10.1007/s40789-016-0122-5
[6]	李楠, 王恩元, GE Mao-chen. 微震監測技術及其在煤礦的應用現狀與展望. 煤炭學報, 2017, 42(增刊1): 83 Li N, Wang E Y, Ge M C. Microseismic monitoring technique and its applications at coal mines: present status and future prospects. J China Coal Soc, 2017, 42(S1): 83
[7]	呂進國, 潘立. 微震預警沖擊地壓的時間序列方法. 煤炭學報, 2010, 35(12):2002 Lü J G, Pan L. Microseismic predicting coal bump by time series method. J China Coal Soc, 2010, 35(12): 2002
[8]	陸菜平, 竇林名, 吳興榮, 等. 巖體微震監測的頻譜分析與信號識別. 巖土工程學報, 2005, 27(7):772 doi: 10.3321/j.issn:1000-4548.2005.07.010 Lu C P, Dou L M, Wu X R, et al. Frequency spectrum analysis on microseismic monitoring and signal differentiation of rock material. Chin J Geotech Eng, 2005, 27(7): 772 doi: 10.3321/j.issn:1000-4548.2005.07.010
[9]	蔡武, 竇林名, 李振雷, 等. 微震多維信息識別與沖擊礦壓時空預測— —以河南義馬躍進煤礦為例. 地球物理學報, 2014, 57(8):2687 doi: 10.6038/cjg20140827 Cai W, Dou L M, Li Z L, et al. Microseismic multidimensional information identification and spatio-temporal forecasting of rock burst: a case study of Yima Yuejin coal mine, Henan, China. Chin J Geophys, 2014, 57(8): 2687 doi: 10.6038/cjg20140827
[10]	郭來功, 戴廣龍, 楊本才, 等. 基于微震成像的采煤工作面應力異常監測. 浙江大學學報: 工學版, 2018, 52(10):2014 Guo L G, Dai G L, Yang B C, et al. Stress anomaly monitoring of coal face based on microseismic tomography. J Zhejiang Univ Eng Sci, 2018, 52(10): 2014
[11]	田向輝, 李振雷, 宋大釗, 等. 某沖擊地壓頻發工作面微震沖擊前兆信息特征及預警方法研究. 巖石力學與工程學報, 2020, 39(12):2471 Tian X H, Li Z L, Song D Z, et al. Study on microseismic precursors and early warning methods of rockbursts in a working face. Chin J Rock Mech Eng, 2020, 39(12): 2471
[12]	喬美英, 程鵬飛, 劉震震. 基于GA-SVM的礦井涌水量預測. 煤田地質與勘探, 2017, 45(6):117 doi: 10.3969/j.issn.1001-1986.2017.06.019 Qiao M Y, Cheng P F, Liu Z Z. Mine water inflow prediction based on GA-SVM. Coal Geol Explor, 2017, 45(6): 117 doi: 10.3969/j.issn.1001-1986.2017.06.019
[13]	趙毅鑫, 楊志良, 馬斌杰, 等. 基于深度學習的大采高工作面礦壓預測分析及模型泛化. 煤炭學報, 2020, 45(1):54 Zhao Y X, Yang Z L, Ma B J, et al. Deep learning prediction and model generalization of ground pressure for deep longwall face with large mining height. J China Coal Soc, 2020, 45(1): 54
[14]	李樹剛, 馬莉, 潘少波, 等. 基于循環神經網絡的煤礦工作面瓦斯濃度預測模型研究. 煤炭科學技術, 2020, 48(1):33 Li S G, Ma L, Pan S B, et al. Research on prediction model of gas concentration based on RNN in coal mining face. Coal Sci Technol, 2020, 48(1): 33
[15]	汪海晉, 尹宗宇, 柯臻錚, 等. 基于一維卷積神經網絡的螺旋銑刀具磨損監測. 浙江大學學報: 工學版, 2020, 54(5):931 Wang H J, Yin Z Y, Ke Z Z, et al. Wear monitoring of helical milling tool based on one-dimensional convolutional neural network. J Zhejiang Univ Eng Sci, 2020, 54(5): 931
[16]	Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks. Shock Vib, 2015(2): 1
[17]	李俊, 劉永葆, 余又紅. 卷積神經網絡和峭度在軸承故障診斷中的應用. 航空動力學報, 2019, 34(11):2423 Li J, Liu Y B, Yu Y H. Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing. J Aerosp Power, 2019, 34(11): 2423
[18]	朱會杰, 王新晴, 芮挺, 等. 基于平移不變CNN的機械故障診斷研究. 振動與沖擊, 2019, 38(5):45 Zhu H J, Wang X Q, Rui T, et al. Machinery fault diagnosis based on shift invariant CNN. J Vib Shock, 2019, 38(5): 45
[19]	Perol T, Gharbi M, Denolle M. Convolutional neural network for earthquake detection and location. Sci Adv, 2018, 4(2): e1700578
[20]	董志鵬, 王密, 李德仁, 等. 遙感影像目標的尺度特征卷積神經網絡識別法. 測繪學報, 2019, 48(10):1285 doi: 10.11947/j.AGCS.2019.20180393 Dong Z P, Wang M, Li D R, et al. Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features. Acta Geod Cartographica Sinica, 2019, 48(10): 1285 doi: 10.11947/j.AGCS.2019.20180393
[21]	趙康寧, 蒲天驕, 王新迎, 等. 基于改進貝葉斯神經網絡的光伏出力概率預測. 電網技術, 2019, 43(12):4377 Zhao K N, Pu T J, Wang X Y, et al. Probabilistic forecasting for photovoltaic power based on improved bayesian neural network. Power Syst Technol, 2019, 43(12): 4377
[22]	金列俊, 詹建明, 陳俊華, 等. 基于一維卷積神經網絡的鉆桿故障診斷. 浙江大學學報: 工學版, 2020, 54(3):467 Jin L J, Zhan J M, Chen J H, et al. Drill pipe fault diagnosis method based on one-dimensional convolutional neural network. J Zhejiang Univ Eng Sci, 2020, 54(3): 467
[23]	高佳豪, 郭瑜, 伍星. 基于SANC和一維卷積神經網絡的齒輪箱軸承故障診斷. 振動與沖擊, 2020, 39(19):204 Gao J H, Guo Y, Wu X. Gearbox bearing fault diagnosis based on SANC and 1-D CNN. J Vib Shock, 2020, 39(19): 204
[24]	夏永學, 康立軍, 齊慶新, 等. 基于微震監測的5個指標及其在沖擊地壓預測中的應用. 煤炭學報, 2010, 35(12):2011 Xia Y X, Kang L J, Qi Q X, et al. Five indexes of microseismic and their application in rock burst forecastion. J China Coal Soc, 2010, 35(12): 2011
[25]	張永峰, 陸志強. 基于集成神經網絡的剩余壽命預測. 工程科學學報, 2020, 42(10):1372 Zhang Y F, Lu Z Q. Remaining useful life prediction based on an integrated neural network. Chin J Eng, 2020, 42(10): 1372
[26]	Kingma D, Ba J. Adam: A method for stochastic optimization // International Conference for Learning Representations. San Diego, 2015: 1
[27]	劉定祥, 喬少杰, 張永清, 等. 不平衡分類的數據采樣方法綜述. 重慶理工大學學報: 自然科學, 2019, 33(7):102 Liu D X, Qiao S J, Zhang Y Q, et al. A survey on data sampling methods in imbalance classification. J Chongqing Univ Technol Nat Sci, 2019, 33(7): 102
[28]	Liu X Y, Wu J X, Zhou Z H. Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B (Cybern), 2009, 39(2): 539 doi: 10.1109/TSMCB.2008.2007853