摘要
利用电测深法能够获得半衰时St/2、衰减度D和视极化率ηs这些与煤系地层含水量相关的指标,通过分析,再引入含水层相对因子参数T"这一指标,选取以上4个物探观测指标作为预测煤系地层含水量的输入参数。通过实例,分别采用多元线性回归模型、人工神经网络模型和最优组合预测模型来预测煤系地层的含水量,研究各个模型的预测精度。结果表明:最优组合预测模型的预测精度最高,证明采用最优组合预测模型预测煤系地层含水量的准确性和实用性。
Using the electrical sounding method, the half decay time, decay rate and apparent polarizability which are relevant to water content of coal-bearing strata can be obtained. Through the analysis, we also introduced aquifer relative factor, select the above four geophysical observation indicators as input parameters to predict the water content of coal-bearing strata. According to instances, multiple linear regression model, artificial neural network model and the optimal combination prediction model were respectiyely used to predict the water content of the coal-bearing strata, and the prediction accuracy of each model is studied. The results show that; the prediction accuracy of the optimal combination prediction model is the highest, and it is effective and practical .
出处
《煤炭技术》
CAS
北大核心
2013年第5期118-120,共3页
Coal Technology
关键词
煤系地层
含水量
组合预测
权系数
coal-bearin
strata
water content
combination prediction
weight coefficient
