HUANG Gang, HAN Yunchun, YU Guofeng, et al. Application research of all fiber optic microseismic monitoring technology in monitoring water inrush from floor[J]. Journal of Mine Automation,2024,50(6):36-45. DOI: 10.13272/j.issn.1671-251x.2024030037
Citation: HUANG Gang, HAN Yunchun, YU Guofeng, et al. Application research of all fiber optic microseismic monitoring technology in monitoring water inrush from floor[J]. Journal of Mine Automation,2024,50(6):36-45. DOI: 10.13272/j.issn.1671-251x.2024030037

Application research of all fiber optic microseismic monitoring technology in monitoring water inrush from floor

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  • Received Date: March 13, 2024
  • Revised Date: June 14, 2024
  • Available Online: July 09, 2024
  • Currently, most fiber optic microseismic monitoring systems in China are based on optical grating sensing technology. However, fiber optic grating wavelength demodulation limits the detection frequency and sensitivity of the system, and there are few successful cases of long-term, continuous and uninterrupted microseismic monitoring. In order to solve the above problems, a new type of all fiber microseismic monitoring system is proposed. Taking the monitoring of water inrush from the floor during the mining process of Pan'er Coal Mine 11023 working face as the engineering background, a comparison is made between the all fiber optic microseismic monitoring system and the ESG microseismic monitoring system. It is found that the all fiber optic microseismic monitoring system has the following advantages. The recorded waveform spectrum features are clearer, showing a high signal-to-noise ratio advantage. The monitoring range for disturbance depth is larger, and the remote monitoring effect is better. The distribution of seismic source positioning results is more reasonable and more in line with the actual mining situation of the working face. During the monitoring of the entire mining cycle of the working face, the relationship between the floor failure and microseismic activity in the fault abnormal area of the 11023 working face is analyzed. Near the fault and coal seam thinning abnormal area, the number and intensity of microseismic events increase. During the initial mining period of the working face, stress is concentrated and released. Due to the influence of mining, the floor is severely damaged. Relatively high energy events are mainly distributed in the floor of the fault anomaly area, with a depth of about 27 meters of damage to the floor. Micro seismic events do not form a line or accumulate in a plane below 60 meters of the 3 coal seam floor. It indicates that cracks have not expanded and no water conducting channels have been formed. The working face can be safely mined.
  • [1]
    袁亮. 我国煤矿安全发展战略研究[J]. 中国煤炭,2021,47(6):1-6.

    YUAN Liang. Study on the development strategy of coal mine safety in China[J]. China Coal,2021,47(6):1-6.
    [2]
    袁亮,吴劲松,杨科. 煤炭安全智能精准开采关键技术与应用[J]. 采矿与安全工程学报,2023,40(5):861-868.

    YUAN Liang,WU Jinsong,YANG Ke. Key technology and its application of coal safety intelligent precision mining[J]. Journal of Mining & Safety Engineering,2023,40(5):861-868.
    [3]
    彭苏萍. 我国煤矿安全高效开采地质保障系统研究现状及展望[J]. 煤炭学报,2020,45(7):2331-2345.

    PENG Suping. Current status and prospects of research on geological assurance system for coal mine safe and high efficient mining[J]. Journal of China Coal Society,2020,45(7):2331-2345.
    [4]
    顾大钊,李庭,李井峰,等. 我国煤矿矿井水处理技术现状与展望[J]. 煤炭科学技术,2021,49(1):11-18.

    GU Dazhao,LI Ting,LI Jingfeng,et al. Current status and prospects of coal mine water treatment technology in China[J]. Coal Science and Technology,2021,49(1):11-18.
    [5]
    袁亮,王恩元,马衍坤,等. 我国煤岩动力灾害研究进展及面临的科技难题[J]. 煤炭学报,2023,48(5):1825-1845.

    YUAN Liang,WANG Enyuan,MA Yankun,et al. Research progress of coal and rock dynamic disasters and scientific and technological problems in China[J]. Journal of China Coal Society,2023,48(5):1825-1845.
    [6]
    张平松,欧元超,李圣林. 我国矿井物探技术及装备的发展现状与思考[J]. 煤炭科学技术,2021,49(7):1-15.

    ZHANG Pingsong,OU Yuanchao,LI Shenglin. Development quo-status and thinking of mine geophysical prospecting technology and equipment in China[J]. Coal Science and Technology,2021,49(7):1-15.
    [7]
    许延春,黄磊. 基于微震监测的工作面底板突水全时空预警方法[J]. 煤炭科学技术,2023,51(1):369-382.

    XU Yanchun,HUANG Lei. Full-time and space early-warning method for floor water inrush in working face based on microseismic monitoring[J]. Coal Science and Technology,2023,51(1):369-382.
    [8]
    肖鹏,韩凯,双海清,等. 基于微震监测的覆岩裂隙演化规律相似模拟试验研究[J]. 煤炭科学技术,2022,50(9):48-56.

    XIAO Peng,HAN Kai,SHUANG Haiqing,et al. Similar material simulation test study on evolution law of overburden fracture based on microseismic monitoring[J]. Coal Science and Technology,2022,50(9):48-56.
    [9]
    武文清. 大采深奥灰水上工作面底板裂隙突水量预测[J]. 煤炭与化工,2020,43(6):58-61.

    WU Wenqing. Prediction of water inrush from floor cracks in working face on deep mining depth[J]. Coal and Chemical Industry,2020,43(6):58-61.
    [10]
    杨作林. 微震信号识别与地压灾害微震前兆规律研究[D]. 赣州:江西理工大学,2015.

    YANG Zuolin. Microseismic signal recognition and the law of ground pressure disaster microseism precursor research[D]. Ganzhou:Jiangxi University of Science and Technology,2015.
    [11]
    查华胜,张海江,连会青,等. 潘二煤矿A组煤层底板灰岩水害微震监测[J]. 煤炭学报,2022,47(8):3001-3014.

    ZHA Huasheng,ZHANG Haijiang,LIAN Huiqing,et al. Microseismic monitoring on limestone water inrush at coal seam floor for group A coal layer of Pan'er Coal Mine[J]. Journal of China Coal Society,2022,47(8):3001-3014.
    [12]
    余国锋,袁亮,任波,等. 底板突水灾害大数据预测预警平台[J]. 煤炭学报,2021,46(11):3502-3514.

    YU Guofeng,YUAN Liang,REN Bo,et al. Big data prediction and early warning platform for floor water inrush disaster[J]. Journal of China Coal Society,2021,46(11):3502-3514.
    [13]
    黄刚. 斜阶跃电流激励下圆锥型场源瞬变电磁AWPSO算法优化反演研究[D]. 南昌:东华理工大学,2021.

    HUANG Gang. Optimization of AWPSO algorithm for conical source transient electromagnetic with ramp step current excitation[D]. Nanchang:East China Institute of Technology,2021.
    [14]
    朱贵旺,任波,余国锋,等. 采动诱发断层带岩体劣化微震响应特征[J]. 煤矿安全,2022,53(5):176-181.

    ZHU Guiwang,REN Bo,YU Guofeng,et al. Micro-seismic response characteristics of rock mass deterioration induced by mining in fault zone[J]. Safety in Coal Mines,2022,53(5):176-181.
    [15]
    柳云龙,田有,冯晅,等. 微震技术与应用研究综述[J]. 地球物理学进展,2013,28(4):1801-1808.

    LIU Yunlong,TIAN You,FENG Xuan,et al. Review of microseism technology and its application[J]. Progress in Geophysics,2013,28(4):1801-1808.
    [16]
    赵向东,陈波,姜福兴. 微地震工程应用研究[J]. 岩石力学与工程学报,2002,21(增刊2):2609-2612.

    ZHAO Xiangdong,CHEN Bo,JIANG Fuxing. Study of micro-seismic engineering applications[J]. Chinese Journal of Rock Mechanics and Engineering,2002,21(S2):2609-2612.
    [17]
    冀贞文,孙春江,姜福兴. 波兰煤矿冲击地压防治技术现状及分析[J]. 煤炭科学技术,2008,36(1):11-14.

    JI Zhenwen,SUN Chunjiang,JIANG Fuxing. Present status and analysis on rock burst prevention and control technology in Poland[J]. Coal Science and Technology,2008,36(1):11-14.
    [18]
    艾纯明,孙振明,吴姗,等. 三维光纤光栅微震加速度传感器研究[J]. 矿业研究与开发,2014,34(6):64-67.

    AI Chunming,SUN Zhenming,WU Shan,et al. Research on 3D optical fiber and grating microseismic acceleration sensor[J]. Mining Research and Development,2014,34(6):64-67.
    [19]
    聂飞,高昕,顾先明,等. 基于光纤传感器的矿井微震信号监测系统设计[J]. 煤炭工程,2015,47(4):19-21.

    NIE Fei,GAO Xin,GU Xianming,et al. Design on mine microseism monitoring system based on optical fiber sensor[J]. Coal Engineering,2015,47(4):19-21.
    [20]
    WANG Jinyu,JIANG Long,SUN Zengrong,et al. Research on the surface subsidence monitoring technology based on fiber bragg grating sensing[J]. Photonic Sensors,2017,7(1):20-26. DOI: 10.1007/s13320-016-0331-y
    [21]
    LIU Tongyu,WEI Yubin,SONG Guangdong,et al. Fibre optic sensors for coal mine hazard detection[J]. Measurement,2018,124:211-223. DOI: 10.1016/j.measurement.2018.03.046
    [22]
    刘统玉,王纪强,孟祥军,等. 面向矿山安全物联网的光纤传感器[J]. 工矿自动化,2018,44(3):1-7.

    LIU Tongyu,WANG Jiqiang,MENG Xiangjun,et al. Optical fiber sensor for mine safety Internet of Things[J]. Industry and Mine Automation,2018,44(3):1-7.
    [23]
    郭清华. 煤矿动力灾害前兆信息传感技术发展与应用[J]. 煤炭科学技术,2022,50(11):76-83.

    GUO Qinghua. Development and application of precursory information sensing technology in coal mine dynamic disaster[J]. Coal Science and Technology,2022,50(11):76-83.
    [24]
    ZHANG Wentao,WANG Zhaogang,HUANG Wenzhu,et al. Fiber laser sensors for micro seismic monitoring[J]. Measurement,2016,79:203-210. DOI: 10.1016/j.measurement.2015.09.046
    [25]
    ZHANG Wentao,LI Fang,LIU Yuliang. Field test of an in-well fiber laser geophone array[C]. 22nd International Conference on Optical Fiber Sensors,Beijing,2012. DOI: 10.1117/12.968588.
    [26]
    ZHANG Wentao,HUANG Wenzhu,LI Fang. Earthquake monitoring using fiber laser borehole seismometer[C]. 22nd International Conference on Optical Fiber Sensors,Beijing,2012. DOI: 10.1117/12.974789.
    [27]
    李世丽. 微震监测用光纤加速度传感器研究[D]. 合肥:安徽大学,2020.

    LI Shili. Research on fiber optic acceleration sensors for microseismic monitoring[D]. Hefei:Anhui University,2020.
    [28]
    王传朋. 基于井上下微震联合监测技术的震源高度误差控制研究[J]. 煤炭工程,2023,55(12):28-33.

    WANG Chuanpeng. Improvement of the error of hypocenter height based on surface-underground microseismic monitoring technology[J]. Coal Engineering,2023,55(12):28-33.
    [29]
    李楠,王恩元,孙珍玉,等. 基于L1范数统计的单纯形微震震源定位方法[J]. 煤炭学报,2014,39(12):2431-2438.

    LI Nan,WANG Enyuan,SUN Zhenyu,et al. Simplex microseismic source location method based on L1 norm statistical standard[J]. Journal of China Coal Society,2014,39(12):2431-2438.
    [30]
    李楠. 微震震源定位的关键因素作用机制及可靠性研究[D]. 徐州:中国矿业大学,2014.

    LI Nan. Research on mechanisms of key factors and reliability for microseismic source location[D]. Xuzhou:China University of Mining and Technology,2014.
    [31]
    平健,李仕雄,陈虹燕,等. 微震定位原理与实现[J]. 金属矿山,2010(1):167-169.

    PING Jian,LI Shixiong,CHEN Hongyan,et al. Principle and realization of microseism location[J]. Metal Mine,2010(1):167-169.
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