摘要
地质统计单元的含矿性与地质找矿证据之间存在复杂的非线性关系,建立这种复杂关系的多元非线性统计模型并预测矿产靶区,对矿产勘查具有重要指导意义。以核函数为理论工具,在核最小二乘原理基础上提出了矿产靶区预测的核最小二乘模型;在GDAL数字图像输入输出函数库和CLAPACK线性代数软件包基础上,用VC++语言开发了面向栅格数据的矿产靶区预测核最小二乘模型算法程序,并把模型应用于新疆阿勒泰地区的矿产靶区预测研究。在MapInfo中生成包含100×151个网格统计单元的栅格图层,把栅格化后的15种找矿证据图层转化成100×151×15的数字图像数据立方体,用自行开发的程序计算每个网格统计单元的核最小二乘判别得分。结果表明,网格统计单元判别得分的高值区与已知矿床(点)的空间分布基本一致。
There exists a complex nonlinear relation between ore-bearing possibilities and prospecting evidences.Predicting mineral targets by modeling this complex relation with multivariate nonlinear statistical models is significant for mineral exploration.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.A raster map layer with 100×151 grid cells is generated in MapInfo.15 evidential raster map layers are transformed into a digital image data cube of size 100×151×15.The discriminant scores derived from kernel minimum error are computed for all the grid cells with the program developed by the authors.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.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2011年第3期937-944,共8页
Journal of Jilin University：Earth Science Edition
基金
国家自然科学基金项目(40872193)
关键词
核函数
核最小二乘法
矿产资源
矿产勘查
靶区预测
kernel function
kernel minimum square error
mineral resources
mineral exploration
target prediction
