煤矿采空区覆岩破裂分布式声波传感监测  

Distributed acoustic sensing monitoring of overburden fractures in coal mine goaf

作　　者：曹凯 吴建宁 卢渊 庞小龙 贺志华 于晓清 王玄 CAO Kai;WU Jianning;LU Yuan;PANG Xiaolong;HE Zhihua;YU Xiaoqing;WANG Xuan(EHV Company of State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750011,China;Power Transformer Engineering Research Institute,China Electric Power Research Institute,Beijing 102401,China)

机构地区：[1]国网宁夏电力有限公司超高压公司,银川750011 [2]中国电力科学研究院有限公司输变电工程研究所,北京102401

出　　处：《地质科技通报》2024年第6期125-135,共11页Bulletin of Geological Science and Technology

基　　金：国网宁夏电力有限公司科技项目“采动影响区重要输电线路杆塔周边地质监测预警与基础变形弹性防治快速矫正技术研究”(5229CG230008)。

摘　　要：煤矿采空区覆岩破裂信号作为开采沉陷的前兆特征,对其开展监测有助于预警采空区塌陷事件。但现有手段难以实现大范围、全方位、分布式的监测。以我国宁东矿区羊场湾煤矿为研究区域,引入分布式声波传感技术(distributed acoustic sensing,简称DAS)对采空区覆岩破裂信号开展连续监测。针对DAS数据信噪较低的问题,对比试验了5种去噪方法。对预处理后的信号开展时频分析,提取覆岩破裂信号;进一步将DAS信号转换为递归图以构建数据集,训练基于卷积神经网络的覆岩破裂信号智能识别模型。结果表明,同步压缩小波变换能够很好地压制DAS数据的噪声。覆岩破裂信号与非覆岩破裂信号的递归图之间具有明显区别,训练得到的VGG-16模型在分类二者的任务上实现了85%的准确率。因此,利用DAS技术监测覆岩破裂具有可行性,本研究所提出的基于递归图和卷积神经网络VGG-16的深度学习方法可实现对覆岩破裂信号的智能识别。研究成果为后续开发基于DAS系统的开采沉陷智能预警系统提供了一定技术支撑。[Objective]As socioeconomic development advances,challenges associated with coal mining beneath structures such as buildings,water bodies,and railways have intensified markedly.It is increasingly imperative to extract underground minerals within acceptable boundaries while diligently monitoring the environmental impacts of such activities.Previous research showed that mining-induced subsidence was a primary contributor to environmen-tal geological disasters in mining areas,particularly when the integrity of the overlying strata is breached.There-fore,it is crucial to develop methodologies for the early detection of surface subsidence,which requires in-depth re-search into monitoring the fracture signals from overburden rock.Existing methods,including acoustic emission and microseismic monitoring systems,face significant challenges in achieving widespread,comprehensive,and distribu-ted monitoring.In response to these limitations,distributed acoustic sensing(DAS),a state-of-the-art optoelec-tronic sensing technique,has recently gained prominence and been extensively employed across geophysical explo-ration fields such as oil and gas exploration and seismic monitoring.We explore applying DAS technology to en-hance the monitoring and identification of fracture signals in the overburden of mined-out areas,aiming to improve both safety and sustainability in mining operations.[Methods]This research selects a coal mine in Ningdong town,Lingwu city,Ningxia Hui Autonomous Region,China.DAS technology is used to continuously monitor the fracture signals of the overburden in underground voids.A fibre optic cable was installed at the bottom of a trench stretching parallel to the coal mining face with dimensions of approximately 1 km in length,15 cm in width,and 30 cm in depth.Additionally,several triaxial node seismometers were deployed along the route for comparison validation.Given the low signal-to-noise ratio of DAS data,comparative experiments were conducted using five denoising tech-niques:high-pass filtering,empirical m

关 键 词：煤矿采空区 分布式声波传感 地球物理探测 覆岩破裂 机器学习 

分 类 号：TD325.3[矿业工程—矿井建设] P631[天文地球—地质矿产勘探]