| 基于支持向量回归机的矿体品位插值 |
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| 引用本文： | 李娟,李翠平,李仲学. 基于支持向量回归机的矿体品位插值[J]. 北京科技大学学报, 2009, 31(12) |
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| 作者姓名： | 李娟 李翠平 李仲学 |
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| 作者单位： | 北京科技大学土木与环境工程学院,北京,100083 |
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| 基金项目： | 国家自然科学基金，教育部高等学校博士学科点专项科研基金，国家科技支撑计划 |
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| 摘 要： | 使用与自组织神经网聚类相结合的支持向量回归机预测模型对矿体体素品位进行插值,并与多边形法、距离幂次反比法、克里格法进行对比验证. 结果表明,该预测模型进行品位插值具备很好的可行性和可靠性.
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| 关 键 词： | 矿体 体视化 品位 支持向量回归机 插值 |
Grade interpolation in orebody based on support vector regression |
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| LI Juan,LI Cui-ping,LI Zhong-xue. Grade interpolation in orebody based on support vector regression[J]. Journal of University of Science and Technology Beijing, 2009, 31(12) |
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| Authors: | LI Juan LI Cui-ping LI Zhong-xue |
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| Affiliation: | LI Juan,LI Cui-ping,LI Zhong-xue School of Civil , Environmental Engineering,University of Science , Technology Beijing,Beijing 100083,China |
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| Abstract: | The method of support vector regression(SVR)in combination with self organization feature mapping(SOFM)network was selected for grade interpolation in orebody,and was compared to the Thiessen polygons method,the distance power inverse ratio method and the Kriging method.The result shows that the prediction model of SVR is feasible and reliable for grade estimation. |
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| Keywords: | orebody volume visualization grade support vector regression space interpolation |
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