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.

中文翻译：

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AUTO-IK：用于地球科学局部不确定性自动非参数建模的 2D 指示克里金程序

指标克里金法提供了一种灵活的插值方法，非常适合以下数据集：1) 许多观测值低于检测限，2) 直方图严重偏斜，或 3) 特定类别的属性值在空间中比其他类别更好地连接（例如污染物浓度低）。然而，要充分利用指标克里金法，需要对多个指标半变异函数进行繁琐的推理和建模，以及对结果进行后处理以检索属性估计值和相关的不确定性度量。本文提出了一种自动执行以下任务的计算机代码：选择连续数据二进制编码的阈值，指标半变异函数的计算和建模，在不受监控的位置（规则或不规则网格）的概率分布建模，以及这些分布的均值和方差的估计。该程序还提供了用于在交叉验证和折叠刀框架内量化不确定性模型的优点的工具。使用来自著名土壤侏罗数据集的重金属浓度来说明不同的功能。敏感性分析表明，当使用线性插值模型实施指标克里金法时，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。使用来自著名土壤侏罗数据集的重金属浓度来说明不同的功能。敏感性分析表明，当使用线性插值模型实施指标克里金法时，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。使用来自著名土壤侏罗数据集的重金属浓度来说明不同的功能。敏感性分析表明，当使用线性插值模型实施指标克里金法时，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。