摘要
为了查明影响潞安矿区煤炭开采沉陷的主要因素,从地质赋存条件和采矿工艺2个方面初选出可能与地表沉陷有关的9个因子,根据潞安矿区10个典型工作面的地质和采矿资料、观测数据,采用灰色关联分析和回归预计方法,分析了地表最大下沉系数与地质因素、采矿因素之间的关系。研究结果表明,潞安矿区煤炭开采沉陷的主要影响因素为覆岩综合硬度、采高、工作面倾向长度及扰动系数;同时以地表最大下沉系数为因变量,以主要影响因素为自变量建立了煤炭开采沉陷回归模型,此模型较好地反映了该地区地表最大下沉系数与其主要影响因素之间的量化关系,同时也为潞安矿区的煤炭开采沉陷预计提供理论依据。
In order to investigate the main factors of coal mining subsidence in Lu’an Mining Area,selecting nine factors that may be related to subsidence from both geological conditions and mining processes,nine factors that may be related to surface subsidence were selected from geological occurrence conditions and mining technology,based on the data of geological,mining and observation of 10 typical working faces in Lu’an Ming Area,by using grey correlation analysis and regression prediction methods,the relationship between the maximum surface subsidence coefficient and factors of geological and mining was analyzed.The research results show that the main influencing factors of coal mining subsidence in Lu’an Ming Area are comprehensive hardness of overburden,mining height,working face inclination length and disturbance coefficient;at the same time,by taking the maximum subsidence coefficient as the dependent variable and the main influencing factors as the independent variable,the regression model of coal mining subsidence is established.The model well reflects the quantitative relationship between the maximum subsidence coefficient and its main influencing factors in this area.It also provides theoretical basis for the prediction of coal mining subsidence in Lu’an Ming Area.
作者
刘东海
邓念东
姚婷
LIU Donghai;DENG Niandong;YAO Ting(College of Geology and Environment,Xi’an University of Science and Technology,Xi’an 710054,China;No.107 Geological Team,Chongqing Bureau of Geology and Mineral Exploration,Chongqing 401120,China)
出处
《矿业安全与环保》
北大核心
2020年第5期103-107,共5页
Mining Safety & Environmental Protection
基金
国家自然科学基金项目(41702377)
中国博士后科学基金项目(2017M623208)
陕西省自然科学基础研究计划项目(2017JQ4008)。
关键词
潞安矿区
采煤沉陷
地质因素
采矿因素
灰色关联度分析
回归预计
Lu’an Mining Area
coal mining subsidence
geological factor
mining factor
gray correlation analysis
regression prediction