Esri, 380 New York Street, Redlands, CA 92373-8100, USA.
Department of Mathematics, Universitat Jaume I, Castellón E-12071, Spain.
Sci Total Environ. 2020 Jun 20;722:137290. doi: 10.1016/j.scitotenv.2020.137290. Epub 2020 Feb 15.
We described the key features of the pragmatic geostatistical methodology aiming at resolving the following drawbacks of classical geostatistical models: assuming that the data is the realization of a stationary process; assuming that the data values are distributed according to Gaussian distribution; describing the data with a single generating model; not accounting for the model uncertainty in prediction; and not supporting coincident data and individual measurement errors. Our variant of empirical Bayesian kriging (EBK) is a fast and reliable solution for both automatic and interactive data interpolation. It can be used for interpolation of very large datasets up to billions of points. The following features are discussed: the informative prior distribution construction and usage; automatic data transformation of the dependent variable into a Gaussian distribution; data subsetting and merging the estimated models; and interpolation over large areas on the earth's surface. We conducted one simulation experiment and two case studies using highly variable data to investigate the EBK predicting quality.
我们描述了实用地质统计学方法的关键特征，旨在解决经典地质统计学模型的以下缺陷：假设数据是平稳过程的实现；假设数据值按照高斯分布分布；使用单一生成模型描述数据；不考虑预测中的模型不确定性；不支持重合数据和个体测量误差。我们的经验贝叶斯克里金（EBK）变体是自动和交互式数据插值的快速可靠解决方案。它可用于对高达数十亿个点的非常大数据集进行插值。讨论了以下特征：信息先验分布的构建和使用；将因变量自动转换为高斯分布；数据子集化和合并估计模型；以及在地球表面的大面积上进行插值。我们使用高度变化的数据进行了一次模拟实验和两个案例研究，以调查 EBK 的预测质量。
