•  
  •  
 

Coal Geology & Exploration

Abstract

The mine Direct Current (DC) resistivity method has the characteristics of high construction efficiency and low cost, which is widely used in the advanced detection of water hazards in coal mine heading face.With the continuous deepening of applications, higher requirements have been put forward for the advanced detection signal processing method of mine DC resistivity method. Based on the response information database obtained by finite element method and data reconstruction, the Levenberg-Marquardt (L-M) artificial neural network method is used to realize the advanced prediction of mine DC resistivity method. First, this study established an advanced prediction model of the DC resistivity method using unstructured mesh and iterative finite element method BiCGSTAB; matched the numerically simulated responses with the measured responses using the empirical mode decomposition (EMD) method; and proposed the prediction mechanism based on the advance detection distance and anomaly rate. As a result, 3 000 sets of 23-dimensional reconstructed responses were obtained. Subsequently, the artificial neural network model is constructed by L-M algorithm. Finally, using the trained artificial neural network, this study predicted the water hazards at the heading face based on the measured data and numerically simulated responses. The results of this study are as follows: (1) For the numerically simulated responses, the mine DC resistivity method based on the L-M artificial neural network can effectively detect the water hazard anomalies within the advance detection distance of 100 m. The mean square error in the detection was 0.002 47, and test samples exhibited that the maximum error of the advance detection distance was less than 0.6 m; (2) As shown by the measured data, the accuracy rate of the detection was 67% in the whole study and was higher than 85% for water hazard anomalies at the advance detection distance of 15-80 m. Therefore, the L-M artificial neural network can be applied to the advance prediction of water disasters based on the mine DC resistivity method. The research results are of great significance for the improvement and wide applications of the mine DC resistivity method-based advance detection of water hazards at the heading face of coal mines.

Keywords

DC resistivity method, advance detection, unstructured mesh generation, response error matching, artificial neural network

DOI

10.12363/issn.1001-1986.22.07.0545

Reference

[1] 程久龙,李飞,彭苏萍,等. 矿井巷道地球物理方法超前探测研究进展与展望[J]. 煤炭学报,2014,39(8):1742−1750.

CHENG Jiulong,LI Fei,PENG Suping,et al. Research progress and development direction on advanced detection in mine roadway working face using geophysical methods[J]. Journal of China Coal Society,2014,39(8):1742−1750.

[2] 韩德品,李丹,程久龙,等. 超前探测灾害性含导水地质构造的直流电法[J]. 煤炭学报,2010,35(4):635−639.

HAN Depin,LI Dan,CHENG Jiulong,et al. DC method of advanced detecting disastrous water–conducting or water–bearing geological structures along same layer[J]. Journal of China Coal Society,2010,35(4):635−639.

[3] 程建远,聂爱兰,张鹏. 煤炭物探技术的主要进展及发展趋势[J]. 煤田地质与勘探,2016,44(6):136−141.

CHENG Jianyuan,NIE Ailan,ZHANG Peng. Outstanding progress and development trend of coal geophysics[J]. Coal Geology & Exploration,2016,44(6):136−141.

[4] 于景邨,刘振庆,廖俊杰,等. 全空间瞬变电磁法在煤矿防治水中的应用[J]. 煤炭科学技术,2011,39(9):110−113.

YU Jingcun,LIU Zhenqing,LIAO Junjie,et al. Application of full space transient electromagnetic method to mine water prevention and control[J]. Coal Science and Technology,2011,39(9):110−113.

[5] 刘青雯. 井下电法超前探测方法及其应用[J]. 煤田地质与勘探,2001,29(5):60−62.

LIU Qingwen. Underground electrical lead survey method and its application[J]. Coal Geology & Exploration,2001,29(5):60−62.

[6] 韩德品,石学锋,石显新,等. 煤矿老窑积水巷道直流电法超前探测异常特征研究[J]. 煤炭科学技术,2019,47(4):157−161.

HAN Depin,SHI Xuefeng,SHI Xianxin,et al. Study on anomaly characteristics of in−advance DC electric detection of water–accumulated roadway in abandoned coal mines[J]. Coal Science and Technology,2019,47(4):157−161.

[7] 张萍芳,高建中,徐少华. 焦作矿区电法探测导、含水构造的效果[J]. 煤田地质与勘探,1997,25(5):40−43.

ZHANG Pingfang,GAO Jianzhong,XU Shaohua. The effect of DC method used to detect water–transmitting and water–bearing structure in Jiaozuo mining area[J]. Coal Geology & Exploration,1997,25(5):40−43.

[8] 程久龙,王玉和,于师建,等. 巷道掘进中电阻率法超前探测原理与应用[J]. 煤田地质与勘探,2000,28(4):60−62.

CHENG Jiulong,WANG Yuhe,YU Shijian,et al. The principle and application of advance surveying in roadway excavation by resistivity method[J]. Coal Geology & Exploration,2000,28(4):60−62.

[9] 高致宏,王信文,何继宾,等. 电法超前探测技术与矿井含水构造精细探测[J]. 煤矿安全,2006,37(9):29−31.

GAO Zhihong,WANG Xinwen,HE Jibin,et al. DC method detection technology and application in detailed detection[J]. Safety in Coal Mines,2006,37(9):29−31.

[10] 黄俊革,王家林,阮百尧. 坑道直流电阻率法超前探测研究[J]. 地球物理学报,2006,49(5):1529−1538.

HUANG Junge,WANG Jialin,RUAN Baiyao. A study on advanced detection using DC resistivity method in tunnel[J]. Chinese Journal of Geophysics,2006,49(5):1529−1538.

[11] 李飞,程久龙,谭强,等. 巷道掘进中电阻率法超前探测研究[J]. 煤矿安全,2012,43(7):30−34.

LI Fei,CHENG Jiulong,TAN Qiang,et al. Study on advanced detection by resistivity method in roadway excavation[J]. Safety in Coal Mines,2012,43(7):30−34.

[12] 张平松,李永盛,胡雄武. 巷道掘进直流电阻率法超前探测技术应用探讨[J]. 地下空间与工程学报,2013,9(1):135−140.

ZHANG Pingsong,LI Yongsheng,HU Xiongwu. Application and discussion of the advanced detection technology with DC resistivity method in tunnel[J]. Chinese Journal of Underground Space and Engineering,2013,9(1):135−140.

[13] 强建科,阮百尧,周俊杰,等. 煤矿巷道直流三极法超前探测的可行性[J]. 地球物理学进展,2011,26(1):320−326.

QIANG Jianke,RUAN Baiyao,ZHOU Junjie,et al. The feasibility of advanced detection using DC three−electrode method in coal−mine tunnel[J]. Progress in Geophysics,2011,26(1):320−326.

[14] 罗国平. 直流电阻率三极超前探测的有效性[J]. 中国煤炭地质,2017,29(3):72−75.

LUO Guoping. Effectiveness of DC resistivity trielectrode advanced prospecting[J]. Coal Geology of China,2017,29(3):72−75.

[15] 李飞,张永超,连会青,等. 掘进工作面直流电法超前探测技术问题探讨[J]. 煤炭科学技术,2020,48(12):250−256.

LI Fei,ZHANG Yongchao,LIAN Huiqing,et al. Discussion on problems of direct current advance detection method in roadway driving face[J]. Coal Science and Technology,2020,48(12):250−256.

[16] 王鹏,鲁晶津,王信文. 再论巷道直流电法超前探测技术的有效性[J]. 煤炭科学技术,2020,48(12):257−263.

WANG Peng,LU Jingjin,WANG Xinwen. Restudy on effectivity of direct current advance detection method in roadway[J]. Coal Science and Technology,2020,48(12):257−263.

[17] NEYAMADPOUR A,ABDULLAH W A T W,TAIB S,et al. 3D inversion of DC data using artificial neural networks[J]. Studia Geophysica et Geodaetica,2010,54(3):465−485.

[18] 高明亮,于生宝,郑建波,等. 基于IGA算法的电阻率神经网络反演成像研究[J]. 地球物理学报,2016,59(11):4372−4382.

GAO Mingliang,YU Shengbao,ZHENG Jianbo,et al. Research of resistivity imaging using neural network based on immune genetic algorithm[J]. Chinese Journal of Geophysics,2016,59(11):4372−4382.

[19] JIANG Feibo,DONG Li,DAI Qianwei. Electrical resistivity imaging inversion:An ISFLA trained kernel principal component wavelet neural network approach[J]. Neural Networks,2018(104):114−123.

[20] 吴易智,范宜仁,巫振观,等. 基于卷积神经网络和MPGA–LM算法的阵列侧向测井快速反演方法[J]. 地球物理学报,2021,64(9):3410−3425.

WU Yizhi,FAN Yiren,WU Zhenguan,et al. A fast inversion method for array laterolog based on convolutional neural network and hybrid MPGA−LM algorithm[J]. Chinese Journal of Geophysics,2021,64(9):3410−3425.

[21] RUCKER C,GUNTHER T,SPITZER K. Three–dimensional modelling and inversion of dc resistivity data incorporating topography–I. Modelling[J]. Geophysical Journal International,2006,166:495−505.

[22] 任政勇,汤井田. 基于局部加密非结构化网格的三维电阻率法有限元数值模拟[J]. 地球物理学报,2009,52(10):2627−2634.

REN Zhengyong,TANG Jingtian. Finite element modeling of 3–D DC resistivity using locally refined unstructured meshes[J]. Chinese Journal of Geophysics,2009,52(10):2627−2634.

[23] 刘洋. 基于非结构网格的电阻率三维正反演及其应用研究[D]. 合肥：中国科学技术大学，2016.

LIU Yang. Study of 3D resistivity modeling and inversion using unstructured grids and their applications[D]. Hefei：University of Science and Technology of China，2016.

[24] VORST H A V D. Bi–CGSTAB:A fast and smoothly converging variant of Bi–CG for the solution of nonsymmetric linear systems[J]. SIAM Journal on Scientific and Statistical Computing,1992,13(2):631−644.

[25] 李金铭. 地电场与电法勘探[M]. 北京：地质出版社，2005.

[26] 王婷. EMD算法研究及其在信号去噪中的应用[D]. 哈尔滨：哈尔滨工程大学，2010.

WANG Ting. Research on EMD algorithm and its application in signal denoising[D]. Harbin：Harbin Engineering University，2010.

[27] 张峤,邓贵仕. Levenberg–Marquardt神经网络在煤矿作业人员人因可靠性评价中应用研究[J]. 大连理工大学学报,2015,55(4):424−430.

ZHANG Qiao,DENG Guishi. Investigation on application of Levenberg–Marquardt neural networks to human reliability evaluation of coalmine workers[J]. Journal of Dalian University of Technology,2015,55(4):424−430.

[28] 彭星煜,刘力升,吕娜,等. 基于BP神经网络的油气长输管道失效概率预测[J]. 全面腐蚀控制,2009,23(5):12−16.

PENG Xingyu,LIU Lisheng,LYU Na,et al. Long−distance oil/gas pipeline failure rate prediction using BP artificial neural network model[J]. Total Corrosion Control,2009,23(5):12−16.

[29] 闫滨,高真伟,强丽峰. 基于L–M算法的BP神经网络在大坝安全监控预报中的应用[J]. 沈阳农业大学学报,2009,40(4):506−509.

YAN Bin,GAO Zhenwei,QIANG Lifeng. Application of BP neural network based on Levenberg–Marquardt algorithm in prediction of dam safety monitoring[J]. Journal of Shenyang Agricultural University,2009,40(4):506−509.

[30] 赵弘,周瑞祥,林廷圻. 基于Levenberg–Marquardt算法的神经网络监督控制[J]. 西安交通大学学报,2002,36(5):523−527.

ZHAO Hong,ZHOU Ruixiang,LIN Tingqi. Neural network supervised control based on Levenberg–Marquardt algorithm[J]. Journal of Xi’an Jiaotong University,2002,36(5):523−527.

Download Chinese Version

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.