Indicator principal component kriging
Indicator principal component kriging
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DOI：
10.1007/bf02082535
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发表时间：
1991-06
期刊：
影响因子：
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通讯作者：
V. Suro-Perez;A. Journel
中科院分区：
文献类型：
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作者：
V. Suro-Perez;A. Journel
提出了一种替代多指标克里格的方法，它通过对原始指标变量的主成分克里格来近似全协指标克里格系统。对高斯模型的这种转换进行了详细的研究。结果表明，主成分之间的相互关系要么不显著，要么完全为零。该结果允许通过克里格主成分推导条件累积密度函数（cdf），然后应用线性反变换。基于真实数据集（Walker Lake）的性能比较表明，所提出的方法实现了近似的条件cdf，相当于指标共克里格，但大大减少了变异函数建模的工作量和较小的计算成本。
An alternative to multiple indicator kriging is proposed which approximates the full coindicator kriging system by kriging the principal components of the original indicator variables. This transformation is studied in detail for the biGaussian model. It is shown that the cross-correlations between principal components are either insignificant or exactly zero. This result allows derivation of the conditional cumulative density function (cdf) by kriging principal components and then applying a linear back transform. A performance comparison based on a real data set (Walker Lake) is presented which suggests that the proposed method achieves approximation of the conditional cdf equivalent to indicator cokriging but with substantially less variogram modeling effort and at smaller computational cost.