AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences.
AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences.
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DOI：
10.1016/j.cageo.2008.08.014
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发表时间：
2009-06
影响因子：
4.4
通讯作者：
Goovaerts P
中科院分区：
文献类型：
--
作者：
Goovaerts P
指标克里格提供了一种灵活的插值方法，非常适合以下情况的数据集：1)许多观测值低于检测极限，2)直方图严重偏斜，或3)特定类别的属性值在空间上比其他属性值连接得更好（例如低污染物浓度）。然而，为了充分发挥指标克里格法的潜力，需要对多指标半方差图进行冗长的推理和建模，以及对结果进行后处理，以检索属性估计和不确定性的相关度量。本文提出了一种计算机代码，它可以自动执行以下任务：连续数据二进制编码的阈值选择，指示器半变函数的计算和建模，非监视位置（规则或不规则网格）的概率分布的建模，以及这些分布的均值和方差的估计。该程序还提供了在交叉验证和折刀框架内量化不确定性模型的优点的工具。不同的功能使用来自著名的土壤Jura数据集的重金属浓度来说明。灵敏度分析表明，当使用线性插值模型实现指标克里格时，使用更多阈值的好处，特别是对于具有正偏斜直方图的变量。
Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
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影响因子：
8.9
作者：
ATTEIA, O;DUBOIS, JP;WEBSTER, R
通讯作者：
WEBSTER, R
影响因子：
11.4
作者：
Goovaerts, Pierre;Trinh, Hoa T.;Adriaens, Peter
通讯作者：
Adriaens, Peter
影响因子：
11.4
作者：
Saito, H;Goovaerts, P
通讯作者：
Goovaerts, P
DOI：
10.1007/bf02089230
发表时间：
1994-04-01
期刊：
MATHEMATICAL GEOLOGY
影响因子：
--
作者：
GOOVAERTS, P
通讯作者：
GOOVAERTS, P
影响因子：
4.4
作者：
Lloyd, CD;Atkinson, PM
通讯作者：
Atkinson, PM