Abstract The decision of stationarity is a fundamental prerequisite to geostatistical estimation and uncertainty characterization of natural resources. Great effort is given to delineate relevant spatial-statistical populations for modeling. Recently developed spatial clustering methodologies claim improvements over traditional methodologies for stationary decision making. However, these novel methods are generally unproven with respect to the geostatistical algorithms that generate the models upon which important and costly decisions are based. In this study, a new method to generate the prior parameter uncertainty for the decision of stationarity is established, and the effects of considering different (and uncertain) decisions of stationarity are explored through a K-Fold cross validation study applied to a nickel laterite dataset with complex multivariate relationships. Results show that, in terms of univariate regression metrics, geostatistical models generated with clustering-based modeling domains are competitive or better than those generated with the traditional merged-lithological domains. However, models generated from the clustering-based stationary domains are superior in terms of multivariate feature reproduction when compared to the merged-lithology domains. We also demonstrate a geostatistical modeling workflow that incorporates uncertainty associated with stationary domaining, with only a minimal extension to established techniques for geostatistical modeling with parameter uncertainty.

中文翻译：

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聚类的性能决定平稳性；镍红土矿床案例研究

摘要 平稳性的判定是自然资源地质统计估计和不确定性表征的基本前提。为描绘相关的空间统计人口以进行建模付出了巨大的努力。最近开发的空间聚类方法声称对静态决策的传统方法有所改进。然而，这些新方法通常未经地质统计算法证实，这些算法生成的模型是重要且代价高昂的决策所依据的模型。在本研究中，建立了一种新的产生用于平稳性决策的先验参数不确定性的方法，通过应用于具有复杂多元关系的镍红土数据集的 K 倍交叉验证研究，探讨了考虑不同（和不确定）的平稳性决策的影响。结果表明，就单变量回归指标而言，使用基于聚类的建模域生成的地质统计模型具有竞争力或优于使用传统合并岩性域生成的地质统计模型。然而，与合并岩性域相比，从基于聚类的静止域生成的模型在多元特征再现方面更为优越。我们还演示了一个地统计建模工作流程，该工作流程包含与固定域相关的不确定性，仅对已建立的具有参数不确定性的地统计建模技术进行了最小程度的扩展。