摘要
为了解各影响因素对煤层瓦斯赋存规律的影响,准确预测煤层瓦斯含量,在分析潘一东勘探钻孔资料的基础上,基于灰色关联分析了影响13-1煤层瓦斯含量的各因素,确定了煤层埋深、顶板岩性、煤层厚度和地质构造是影响煤层瓦斯含量的主要因素;利用神经网络方法建立了煤层瓦斯含量预测模型,结合实际数据,对预测模型进行训练与检验。结果表明:预测精度较高,验证了基于灰色理论与神经网络预测模型的可靠性。
In order to Rnow variour factors that affects gas regularity,and to predict gas content accurately,this paper analyses influence factors with grey relational grade base on the analysis on the exploration borehole iuformation of 13-1 seam in Panyi east coal mine.It is found that the main factors affecting gas content in the coal seam are depth of coal seam occurrence,roof lithology,thickness of coal seam and tectonic structure.A prediction model of gas content was established with neural network method,and the model was trained and tested with experimental data.The results showed that the model is accurate,which verified reliability of the prediction model based on grey theory and neural network.
出处
《安徽理工大学学报（自然科学版）》
CAS
2010年第4期1-4,共4页
Journal of Anhui University of Science and Technology:Natural Science
基金
安徽省教育厅自然科学基金资助项目(2006kj002B)
安徽省科技厅科技攻关计划重点资助项目(04022007)
关键词
瓦斯含量
灰色关联度
神经网络
预测模型
gas content
gray relational grade
neural network
prediction model
