基于深度学习的地震多属性融合技术在导水裂隙带探测中的应用  

Application of deep learning-based seismic multi-attribute fusion technology in the detection of water conducting fissure zone

作　　者：袁峰 申涛 谢晓深 马丽 汶小岗 YUAN Feng;SHEN Tao;XIE Xiaoshen;MA Li;WEN Xiaogang(Key Laboratory of Coal Resources Exploration and Comprehensive Utilization,Ministry of Land and Resources,Xi’an 710026,China;Shaanxi Provincial Coal Geophysical Prospecting,Surveying and Mapping Co.,Ltd.,Xi’an 710005,China;Shaanxi Provincial Geological Survey Institute,Xi’an 710065,China;College of Geology and Environment,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]自然资源部煤炭资源勘查与综合利用重点实验室,陕西西安710026 [2]陕西省煤田物探测绘有限公司,陕西西安710005 [3]陕西省地质调查院,陕西西安710065 [4]西安科技大学地质与环境学院,陕西西安710054

出　　处：《煤炭学报》2021年第10期3234-3244,共11页Journal of China Coal Society

基　　金：陕西省技术创新引导专项基金资助项目(2019CGHJ-20);陕西省煤炭绿色开发地质保障重点实验室开放基金资助项目(DZBZ2020-01);陕西煤业化工集团科研基金资助项目(2018SMHKJ-B-J-24)。

摘　　要：导水裂隙带发育高度是“保水采煤”的基础参数,准确揭示导水裂隙带发育规律有助于实现保水开采。三维地震技术具有数据横向连续、纵向分辨率较高的特点,在一定程度上能够弥补钻孔资料的不足。为了研究陕北某煤矿不同开采阶段导水裂隙带发育范围及其演化规律,以该矿30101,30102工作面为研究对象,采用以漏失量为监督数据的深度前馈神经网络方法融合多种地震属性对研究区分层开采全垮落式采动损害导水裂隙带发育高度进行探测。首先,在钻孔冲洗液漏失量观测数据分析的基础上,优选地震属性,并进行有监督的深度学习;然后,融合多种属性信息建立裂隙模型,利用裂隙模型分析确定采动覆岩结构破坏和导水裂隙带的三维空间范围、形态特征、垂向岩石破坏程度及导水裂隙带发育高度;最后,通过分析2个工作面的开采时间差异,揭示导水裂隙的发育、闭合规律。研究表明30101工作面断裂带发育最大高度为120 m,30102工作面断裂带发育最大高度为133 m,以高角度裂隙为主,主要沿垂直、平行工作面方向发育。导水裂隙带的裂隙发育是先增大后降低,断裂带上部裂隙闭合较好,断裂带下部和工作面边缘裂隙闭合较差,采动后18个月裂隙比采动后4个月减少了21%,离层裂隙减少了50%。通过本次研究认为基于深度学习的地震属性融合技术对煤层采动引起的岩层破坏规律探测具有针对性,其精度能满足生产需要,费用低,是一种有效探测技术。The height of water-conducting fractured zone is a basic parameter for the study of water-preserved coal mining.Accurately revealing the development law of water-conducting fracture zone is helpful to realize water-preserved coal mining.The three-dimensional seismic technology has the characteristics of horizontal continuity and high vertical data resolution.To a certain extent,the data obtained by three-dimensional seismic technology can compensate for the scarcity of the data obtained by drilling.In order to study the scale and the law of evolution of water-conducting fracture zone at different mining stages,the 30101 working face and the 30102 working face in a coal mine in northern Shaanxi were taken as example,the neural network method of depth feeder with leakage as monitoring data was used to detect the development height of water-conducting fracture by coal mining in the study area.First of all,combined with drilling fluid data,the deep learning of seismic properties,which were selected by analyzing the drilling fluid data,were carried out.Then,the fracture model was established by merging various attribute information,and the three-dimensional spatial range,morphological characteristics,vertical rock failure degree and development height of water-conducting fracture zone were determined by the model.Finally,the laws of development and closure of the water conductive fracture were inferred through analyzing the mining time different between the two working faces.The research shows that the maximum height of fracture zone in the 30101 working face is 120 m,and that of the 30102 working face is 133 m.Fractures with high-angle are developed in water-conducting fracture zone along the vertical and parallel working face direction.The fracture development in the water-conducting fracture zone increases first and then decreases,the fracture closure in the upper part of the fracture zone is better,and the fracture closure in the lower part of the fracture zone and the edge of the working face is worse.The number

关 键 词：深度学习 多属性融合技术 导水裂隙带 三维地震技术 保水采煤 

分 类 号：TD745[矿业工程—矿井通风与安全]