Volume 49 Issue 3
Mar.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(3): 63-69
LI Tieniu, HU Binxin, LI Huakun, et al. Automatic picking method of microseismic first arrival time based on improved support vector machine[J]. Journal of Mine Automation，2023，49(3)：63-69.  doi: 10.13272/j.issn.1671-251x.2022050081
Citation: LI Tieniu, HU Binxin, LI Huakun, et al. Automatic picking method of microseismic first arrival time based on improved support vector machine[J]. Journal of Mine Automation，2023，49(3)：63-69.  doi: 10.13272/j.issn.1671-251x.2022050081
shu

Automatic picking method of microseismic first arrival time based on improved support vector machine

doi: 10.13272/j.issn.1671-251x.2022050081
  • 1.

    Laser Research Institute, Qilu University of Technology(Shandong Academy of Sciences), Jinan 250300, China

  • 2.

    Chifeng Jilong Mining Co., Ltd., Chifeng 024328, China

  • 3.

    Shandong Shenglong Safety Technology Co., Ltd., Jinan 250032, China

  • 4.

    Shandong Hengbang Mining Development Co., Ltd., Yantai 264199, China

  • Received Date: 2022-05-30
  • Rev Recd Date: 2023-03-09
    • Available Online: 2022-10-14
    Abstract
  • The microseismic first arrival time picking is an important prerequisite for the high-precision positioning of the microseismic source. The traditional manual picking method is inefficient. The automatic picking method is difficult to pick the arrival time of the first wave accurately under the condition of low signal-to-noise ratio. In order to solve the above problems, an automatic picking method of microseismic first arrival time based on improved support vector machine (SVM) is proposed. Firstly, the method carries out normalization processing, linear correction and proper clipping on original microseismic data. The method marks different categories of the data by taking the amplitude, the energy and the energy ratio of adjacent moments of the microseismic data as features. Secondly, the method adopts a particle swarm optimization (PSO) algorithm and a grid search method to optimize the penalty parameters and the kernel function parameters of the SVM. The method carries out large-range fast positioning on the parameters by using the PSO algorithm to obtain a preliminary optimal solution. Then the method re-constructs a parameter search interval by taking the solution as an initial position, sets a small-step grid search method to carry out fine searching on the parameters to obtain the optimal parameters. The method substitutes the optimal parameters into the SVM model to train, and obtains the improved SVM model. Finally, the microseismic data are classified and identified according to the improved SVM model. The time corresponding to the first sampling point of the microseismic wave is defined as the arrival time of the first wave. The microseismic monitoring data from a mine shaft is used for the experiment. The results show that the accuracy of the method for picking the microseismic first arrival time is 96.5%, and the average picking error is 3.8 ms. Under the condition of low signal-to-noise ratio, the microseismic first arrival time can still be picked accurately. The picking precision is higher than the short term average/long term average (STA/LTA) method commonly used in automatic picking methods.

     

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    • References(22)
  • [1]
    孙继平,程继杰. 煤矿冲击地压和煤与瓦斯突出感知报警方法研究[J]. 工矿自动化,2022,48(1):1-6. doi: 10.13272/j.issn.1671-251x.17881

    SUN Jiping,CHENG Jijie. Study on the perception and alarm method of coal mine rock burst and coal and gas outburst[J]. Industry and Mine Automation,2022,48(1):1-6. doi: 10.13272/j.issn.1671-251x.17881
    [2]
    马天辉,唐春安,唐烈先,等. 基于微震监测技术的岩爆预测机制研究[J]. 岩石力学与工程学报,2016,35(3):470-483.

    MA Tianhui,TANG Chun'an,TANG Liexian,et al. Mechanism of rock burst forecasting based on micro-seismic monitoring technology[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(3):470-483.
    [3]
    刘强,蔡永顺,石峰,等. 微震监测技术在某金矿的应用与研究[J]. 中国矿业,2020,29(增刊1):145-148.

    LIU Qiang,CAI Yongshun,SHI Feng,et al. Application and study of microseismic monitoring technology in a gold mine[J]. China Mining Magazine,2020,29(S1):145-148.
    [4]
    WAMRIEW D,DORHJIE D B,BOGOEDOV D,et al. Microseismic monitoring and analysis using cutting-edge technology:a key enabler for reservoir characterization[J]. Remote Sensing,2022,14(14):3417-3438. doi: 10.3390/rs14143417
    [5]
    LIU Fei,MA Tianhui,TANG Chun'an,et al. Prediction of rockburst in tunnels at the Jinping II hydropower station using microseismic monitoring technique[J]. Tunnelling and Underground Space Technology,2018,81:480-493. doi: 10.1016/j.tust.2018.08.010
    [6]
    李楠,王恩元,GE Maochen. 微震监测技术及其在煤矿的应用现状与展望[J]. 煤炭学报,2017,42(增刊1):83-96. doi: 10.13225/j.cnki.jccs.2016.0852

    LI Nan,WANG Enyuan,GE Maochen. Microseismic monitoring technique and its applications at coal mines:present status and future prospects[J]. Journal of China Coal Society,2017,42(S1):83-96. doi: 10.13225/j.cnki.jccs.2016.0852
    [7]
    GESRET A,DESASSIS N,NOBLE M,et al. Propagation of the velocity model uncertainties to the seismic event location[J]. Geophysical Journal International,2014(1):52-66.
    [8]
    宋广东. 基于光纤传感的微震信号采集和识别与震源定位研究[D]. 北京: 中国矿业大学（北京）, 2019.

    SONG Guangdong. Research on acquisition and recognition of microseismic signal and source location based on optical fiber sensing[D]. Beijing: China University of Mining and Technology-Beijing, 2019.
    [9]
    STEFANO R D,ALDERSONS F,KISSLING E,et al. Automatic seismic phase picking and consistent observation error assessment:application to the Italian seismicity[J]. Geophysical Journal International,2006,165(1):121-134. doi: 10.1111/j.1365-246X.2005.02799.x
    [10]
    贾瑞生,谭云亮,孙红梅,等. 低信噪比微震P波震相初至自动拾取方法[J]. 煤炭学报,2015,40(8):1845-1852. doi: 10.13225/j.cnki.jccs.2014.1122

    JIA Ruisheng,TAN Yunliang,SUN Hongmei,et al. Method of automatic detection on micro-seismic P-arrival time under low signal-to-noise ratio[J]. Journal of China Coal Society,2015,40(8):1845-1852. doi: 10.13225/j.cnki.jccs.2014.1122
    [11]
    高新成, 杜功鑫, 王莉利, 等. 深度学习在地震初至拾取中的应用综述[J]. 计算机技术与发展, 2022, 32（8）: 1-6.

    GAO Xincheng, DU Gongxin, WANG Lili, et al. Application of deep learning in earthquake first break picking[J]. Computer Technology and Development, 2022, 32（8）: 1-6.
    [12]
    LEE M,BYUN J,KIM D,et al. Improved modified energy ratio method using a multi-window approach for accurate arrival picking[J]. Journal of Applied Geophysics,2017,139:117-130. doi: 10.1016/j.jappgeo.2017.02.019
    [13]
    MARTON S,LUDTKE S,BARTELT C. Explanations for neural networks by neural networks[J]. Applied Sciences,2022,12(3):980-993. doi: 10.3390/app12030980
    [14]
    刘佳楠,武杰. 基于全卷积神经网络的地震初至波拾取[J]. 信息技术与网络安全,2018,37(11):58-63.

    LIU Jianan,WU Jie. Seismic first arrival picking based on full convolutional networks[J]. Information Technology and Network Security,2018,37(11):58-63.
    [15]
    李洪丽,张晗. 基于长短时均值比法、偏振法和AIC法三种微地震初至拾取方法的对比[J]. 世界地质,2020,39(3):649-655,663. doi: 10.3969/j.issn.1004-5589.2020.03.014

    LI Hongli,ZHANG Han. Comparison of three microseismic first arrival picking methods based on ratio of short-term average and long-term average,polarization method and AIC method[J]. Global Geology,2020,39(3):649-655,663. doi: 10.3969/j.issn.1004-5589.2020.03.014
    [16]
    王洪超. 基于Fast−AIC算法的微地震事件初至拾取及自动识别技术研究[D]. 长春: 吉林大学, 2017.

    WANG Hongchao. Research on automatic microseismic event detection and arrival picking based on Fast-AIC algorithm[D]. Changchun: Jilin University, 2017.
    [17]
    高煜,胡宾鑫,朱峰,等. 微震初至波到时自动拾取研究[J]. 工矿自动化,2020,46(12):106-110. doi: 10.13272/j.issn.1671-251x.17564

    GAO Yu,HU Binxin,ZHU Feng,et al. Research on automatic picking of microseismic first arrival[J]. Industry and Mine Automation,2020,46(12):106-110. doi: 10.13272/j.issn.1671-251x.17564
    [18]
    李亦滔. 基于支持向量机的改进分类算法[J]. 计算机系统应用,2019,28(10):145-151. doi: 10.15888/j.cnki.csa.007080

    LI Yitao. Improved classification algorithm based on support vector machine[J]. Computer System & Applications,2019,28(10):145-151. doi: 10.15888/j.cnki.csa.007080
    [19]
    丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报,2011,40(1):2-10.

    DING Shifei,QI Bingjuan,TAN Hongyan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China,2011,40(1):2-10.
    [20]
    MA Qian, SUN Cong, CUI Baojiang, et al. A novel model for anomaly detection in network traffic based on kernel support vector machine[J]. Computers & Security, 2021, 104. DOI: 10.1016/J.COSE.2021.102215.
    [21]
    LYU Zhiming,ZHAO Jun,WANG Wei,et al. A multiple surrogates based PSO algorithm[J]. Artificial Intelligence Review,2019,52(4):2169-2190. doi: 10.1007/s10462-017-9601-3
    [22]
    SUN Yuting,DING Shifei,ZHANG Zichen,et al. An improved grid search algorithm to optimize SVR for prediction[J]. Soft Computing,2021(10):5633-5644.
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