中文摘要：

针对常规克里金插值方法中变异函数为有限确定函数,难以准确刻画实际数据分布（特别是复杂要素资料的空间结构）的不足,基于最小二乘支持向量机从实际资料场中拟合重构变异函数的研究思想,提出了一种改进的插值方法——支持向量机—克里金插值算法（SVM-Kriging）。采用常规的克里金方法和支持向量机—克里金插值方法（SVM-Kriging）进行插值试验和对比分析,结果表明：支持向量机—克里金方法（SVM-Kriging）的变异函数源自实际的数据场,它克服了常规克里金插值对变异函数选择的依赖性以及变异函数选择的主观性和人为性,表现出较好的针对性和客观性,较为有效地改善了插值效果。

英文摘要：

The traditional Kriging interpolation method contains only limited types of variogram models to express the spatial variation of variable,which is difficult to depict the distribution of actual data exactly（especially for the spatial structure of complicated data） .A Support Vector Machine-Kriging（SVM-Kriging） method is proposed in this paper by introducing Least Square Support Vector Machine（LS-SVM） to fit the experimental variogram of actual data.The SVM-Kriging method is compared with the traditional Kringing method,and the results show that the variogram of SVM-Kriging method from actual data fields can avoid the subjectivity and arbitrariness of selecting types of variogram models.The SVM-Kriging method has good objectivity and adaptability,effectively improving the Kriging interpolation results.