Mineral Target Prediction Based on Kernel Minimum Square
详细信息
- 责任者：Chen Yongliang (Mineral Resources Institute of Comprehensive Information Prediction ; Jilin University ; Changchun 130026 ; China) ; Li Xuebin
- 刊名：Journal of Jilin University
- 出版年：2011
- 卷期：41(3)
- 关键词：成矿预测 ; metallogenic prediction ; 最小二乘法 ; least-squares methods
- 页码：p.937-944
- 图表：3 illus., 1 table, 27 refs.
- 分类号：1900
- issnNo：ISSN1671-5888
- isbnNo：CN22-1343/P
摘要
In this paper, the authors propose a kernel minimum square error model for mineral target prediction on the basis of kernel function theories and kernel minimum square error. A VC++ program for raster data oriented mineral target prediction with minimum square error algorithm is developed on the basis of GDAL, a C++ library for the input and output of digital image data, and CLAPACK, a linear algebra software package. The model has been applied to the mineral target prediction in Altay, northern Xinjiang. It is shown that the areas with high discriminant scores coincide with the known mineral occurrences. Thus, the kernel minimum square error model is feasible for multivariate nonlinear mineral target prediction.