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煤层瓦斯含量是矿井瓦斯灾害防治的主要参数之一,影响其分布特征的地质因素有很多。利用灰色理论的灰色关联分析法对选取的8个影响煤层瓦斯含量的地质因素进行了分析,筛选出断距、埋深、基岩厚度以及挥发分4个主要影响因素,并将其作为BP神经网络模型的输入端建立了煤层瓦斯含量预测模型。对该预测模型进行训练与仿真检验,并与传统的多元线性回归预测方法进行比较分析。
Gas content in coal seam is one of the main parameters of mine gas disaster prevention and control. There are many geological factors that affect its distribution characteristics. Gray relational analysis of gray theory was used to analyze the geological factors affecting the gas content in the coal seams. Four main influencing factors of fault distance, depth, bedrock thickness and volatile matter were selected and used as BP nerve At the input of network model, a prediction model of gas content in coal seam is established. The forecasting model is trained and simulated, and compared with the traditional multiple linear regression forecasting method.