【作者】 肖良；

【导师】 夏玉成；

【作者基本信息】 西安科技大学 ， 环境科学， 2011， 硕士

【摘要】 基于神东矿区具体地质条件,通过理论分析、借助计算机数值模拟、科学计算方法,研究神东矿区开采沉陷的主控因素,将其引入到下沉系数的预计当中,推导出在一定开采条件下沙基比、松散层厚度等地质因素与采煤沉陷下沉系数的预计关系式,并提出开采沉陷下沉系数的GA-WNN(遗传小波神经网络)预计模型。根据神东矿区煤层赋存特点,借助模糊层次分析可知,神东矿区开采沉陷的主控因素为:沙基比、松散层厚度、采厚、关键层的类型及位置、覆岩综合硬度,其影响权重分别为:0.2211、0.1538、0.1489、0.1138、0.0861。在神东矿区达到充分采动的情况下,若不考虑采矿因素,覆岩综合硬度与开采沉陷下沉系数成反比关系;沙基比、松散层厚度与开采沉陷下沉系数成正比关系。在覆岩综合硬度较难计算的情况下,可利用沙基比λ、松散层厚度χ对开采沉陷下沉系数η进行预计,预计公式如下:利用遗传算法(GA——Genetic Algorithm)优化小波(W——Wavelet)神经网络(NN——Neural Network),建立开采沉陷下沉系数预计模型GA-WNN,其预计结果与实际观测值基本符合,精度较高,适用于神东矿区开采沉陷下沉系数的预计。更多还原

【Abstract】 According to gological conditions of Shendong mining area, the main controlling factors of mining subsidence are selected and introduced into the models of mining subsidence prediction by means of theoretical analysis, numerical simulations and scientific programs. And the relation between subsidence coefficient and the main controlling factors are derived; the Optimized Wavelet Neural Network based on Genetic Algorithm (GA-WNN) is applied to the prediction of mining subsidence.Using improved fuzzy analytical hierarchy process, the master-factors of mining subsidence are selected as the ratio and thickness of losses bed, mining thick, comprehensive hardness of cover rocks, the position and type of key stratum, each of whose weight are 0.2211、0.1538、0.1489、0.1138、0.0861.Under the full minnig of the same intensity, subsidence coefficient is inversely proportional to comprehensive hardness of cover rocks, and proportional to the ratio and thickness of losses bed. When the comprehensive hardness of cover rocks is difficult to determine, the subsidence coefficient (η) is predicted using the ratio (λ) and thickness (χ) of losses bed, the regression equation is as followes:Based on GA-WNN and numerical simulations, the other model of mining subsidence prediction is conducted. The practical simulation results show that GA-WNN model can effectively increase the diagnostic accuracy, which results are close to actual experiences. The model applies to prediction of mining subsidence.更多还原