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| 基于RBF神经网络的矿产资源潜力预测模型 | |
| 引用本文： | 戴黎明,陈永良,刘鑫,周均太,赵峰梅,索艳慧,高武斌,楼达.基于RBF神经网络的矿产资源潜力预测模型[J].物探与化探,2011,35(1):103-108. |
| 作者姓名： | 戴黎明 陈永良 刘鑫 周均太 赵峰梅 索艳慧 高武斌 楼达 |
| 作者单位： | 1. 中国海洋大学海洋地球科学学院,山东,青岛,266100;海底科学与探测技术教育部重点实验室,山东,青岛,266100 2. 吉林大学地球探测科学与技术学院,吉林,长春,130026 3. 中国人民解放军57015部队,北京,100082 4. 中国石油大港油田公司,天津,300280 |
| 基金项目： | 国家自然科学基金(40471086); 吉林大学校内创新工程基金(419070200044) |
| 摘 要： | 提出一种基于RBF神经网络的矿产资源潜力制图模型。应用该模型生成矿产资源潜力分布图分三步完成：第一步，以找矿标志的空间分布图和已知矿点空间分布图为依据，提取训练样本；第二步，根据训练样本构建RBF矿产资源潜力制图模型；第三步，生成矿产资源潜力分布图。笔者以新疆北部阿尔泰多金属成矿带为研究区，比较了该模型与合成有矿可信度等模型的找矿靶区圈定结果。两种模型的靶区圈定结果基本相同，证明了RBF矿产资源潜力制图模型的有效性。 |
| 关 键 词： | RBF神经网络 合成有矿可信度模型 矿产资源潜力 |
| 收稿时间： | 2010-01-09 |
A MINERAL RESOURCE POTENTIAL MAPPING MODEL BESED ON RBF NEURAL NETWORKS |
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| DAI Li-ming,CHEN Yong-liang,LIU Xin,ZHOU Jun-tai,ZHAO Feng-mei,SUO Yan-hui,GAO Wu-bin,LOU Da.A MINERAL RESOURCE POTENTIAL MAPPING MODEL BESED ON RBF NEURAL NETWORKS[J].Geophysical and Geochemical Exploration,2011,35(1):103-108. | |
| Authors: | DAI Li-ming CHEN Yong-liang LIU Xin ZHOU Jun-tai ZHAO Feng-mei SUO Yan-hui GAO Wu-bin LOU Da |
| Institution: | DAI Li-ming1,2,CHEN Yong-liang3,LIU Xin1,ZHOU Jun-tai1,ZHAO Feng-mei1,SUO Yan-hui1,GAO Wu-bin4,LOU Da5(1.College of Marine Geosciences,Ocean University of China,Qingdao 266100,China,2.Key Lab of Submarine Geosciences and Prospecting Techni-ques,Ministry of Education,3.College of Earth Sciences,Jilin University,Changchun 130026,4.Troops 57015,Chinese People's Liberation Army,Beijing 100082,5.Dagang Oil-Gas Company,CNPC,Dagang 300280,China) |
| Abstract: | A new RBF neural networks model for mineral resource potential mapping is proposed in this paper.For the purpose of applying this new model,a three-step procedure is needed as follows: the first step is to get training samples from the study area;the second step is to abstract the structure of spatial information of training samples and then to construct a RBF networks;the last step is to generate the distributive map of mineral resource potentials.In this paper,the model was employed to predict multi-metal... |
| Keywords: | RBF networks model C-F model mineral resource potential |
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