Compensating for estimation smoothing in kriging
Compensating for estimation smoothing in kriging
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DOI：
10.1007/bf02083653
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发表时间：
1996-05
期刊：
影响因子：
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通讯作者：
R. Olea;V. Pawlowsky
中科院分区：
文献类型：
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作者：
R. Olea;V. Pawlowsky
平滑是所有最小均方误差空间估计器（例如克里金法）固有的特征。交叉验证可用于检测和建模此类平滑。模型的反演产生了一个新的估计量——补偿克里金法。基于 4 英尺 2 伯里亚砂岩板的详尽渗透率采样的数值比较表明，补偿克里金法生成的估计表面的属性介于普通克里金法生成的属性和模拟退火和顺序高斯模拟产生的随机实现之间。补偿克里金曲面可以很好地再现频率分布，这也可以很好地逼近实验半变异函数——比普通克里金更好，但不如随机实现。补偿克里金法生成的曲面比随机实现更准确，但不如普通克里金法准确。
Smoothing is a characteristic inherent to all minimum mean-square-error spatial estimators such as kriging. Cross-validation can be used to detect and model such smoothing. Inversion of the model produces a new estimator—compensated kriging. A numerical comparison based on an exhaustive permeability sampling of a 4-ft2slab of Berea Sandstone shows that the estimation surface generated by compensated kriging has properties intermediate between those generated by ordinary kriging and stochastic realizations resulting from simulated annealing and sequential Gaussian simulation. The frequency distribution is well reproduced by the compensated kriging surface, which also approximates the experimental semivariogram well—better than ordinary kriging, but not as well as stochastic realizations. Compensated kriging produces surfaces that are more accurate than stochastic realizations, but not as accurate as ordinary kriging.