| 基于BP神经网络的瓦斯含量预测 |
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| 引用本文： | 吴观茂,黄明,李刚.基于BP神经网络的瓦斯含量预测[J].煤田地质与勘探,2008,36(1):30-33. |
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| 作者姓名： | 吴观茂 黄明 李刚 |
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| 作者单位： | 1. 中国矿业大学煤炭资源与安全开采国家重点实验室, 北京 100083; |
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| 基金项目： | 国家自然科学基金
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国家科技攻关项目 |
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| 摘 要： | 以淮南矿区潘一矿13-1煤层为研究对象,在分析勘探钻孔资料的基础上,确定了煤层埋深及厚度、顶底板岩性、地质构造和煤变质程度是影响煤层瓦斯含量的主要因素;使用BP神经网络方法建立了瓦斯含量预测模型;结合实际数据,对预测模型进行训练和检验。预测结果表明:该模型比使用多元线性回归预测能获得更高的精度,说明预测模型可靠。
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| 关 键 词： | 瓦斯含量 BP神经网络 预测模型 |
| 文章编号： | 1001-1986（2008）01-0030-04 |
| 收稿时间： | 2007-08-03 |
| 修稿时间： | 2007年8月3日 |
Gas content prediction based on BP neural network |
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| WU Guan-mao,HUANG Ming,LI Gang.Gas content prediction based on BP neural network[J].Coal Geology & Exploration,2008,36(1):30-33. |
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| Authors: | WU Guan-mao HUANG Ming LI Gang |
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| Institution: | 1. Laboratory of Coal Resources and Mine Safety, CUMT, Beijing 100083, China;2. College of Computer Science, Anhui University of Science and Technology, Huainan 232001, China |
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| Abstract: | The paper presented a BP neural network model to predict content of coalbed gas based on analyzing explored borehole data of No.13-1 coal bed of Panyi mine in Huainan coal mining area.It is determined that the coal burial depth,thickness,lithologic properties of roof and floor,and coal rank are the important factors controlling gas content of coal.And the model was trained and tested with experimental data.Predicting results of the model are more accurate and reliable than that of linear regression prediction model. |
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| Keywords: | gas content BP neural network prediction model |
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