Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA.
Environ Sci Technol. 2009 Dec 15;43(24):9267-73. doi: 10.1021/es901431y.
Information about attributes such as contaminant concentrations or hydraulic properties in benthic sediments is typically obtained in core sections of varying lengths, and only the average value is measured in each section. However, an estimate of the attribute distribution at a uniform spatial resolution is often required for site characterization and the design of appropriate risk-based remediation alternatives. Because attributes exhibit spatial autocorrelation, geostatistical methods have become an essential tool for estimating their spatial distribution. The purpose of this paper is to optimally infer the spatial distribution of sampled attributes at a uniform resolution from fluvial core sampling data, using a downscaling technique formulated as a geostatistical inverse problem. We compare geostatistical downscaling to the more traditional approach of point-to-point ordinary kriging for a hypothetical case study, and for total organic carbon observations from the Passaic River, New Jersey. Although frequently used to interpolate measurements, ordinary kriging is shown not to be able to estimate the spatial distribution of attributes accurately, because this approach assumes that data are sampled at a uniform resolution. Geostatistical downscaling, on the other hand, is shown to resolve this problem by explicitly accounting for the relationship between the known average measurements and the unknown fine-resolution attribute distribution to be estimated.
底栖沉积物中污染物浓度或水力性质等属性的信息通常是在不同长度的岩芯段中获得的，并且在每个岩芯段中仅测量平均值。然而，为了进行场地特征描述和设计适当的基于风险的修复替代方案，通常需要以均匀的空间分辨率估计属性的分布。由于属性表现出空间自相关，因此地统计学方法已成为估计其空间分布的重要工具。本文的目的是使用一种下推技术（表述为地统计学反问题），从河流岩芯采样数据中以均匀分辨率最佳地推断采样属性的空间分布。我们将地统计学下推与更传统的点对点普通克里金方法进行了比较，后者是新泽西西皮考克河总有机碳观测的假设案例研究。虽然普通克里金经常用于插值测量，但事实证明，由于该方法假定数据是在均匀分辨率下采样的，因此无法准确估计属性的空间分布。另一方面，地统计学下推通过显式考虑已知平均测量值与要估计的未知精细分辨率属性分布之间的关系，解决了这个问题。
