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Interpolation and mapping of probabilities for geochemical variables exhibiting spatial intermittency

机译:具有空间间歇性的地球化学变量的概率插值和映射

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摘要

In monitoring a minor geochemical element in groundwater or soils. a background population of values below the instrumental detection limit is frequently present. When those values are found in the monitoring process, they, are assigned to the detection limit which, in some cases, generates a probability mass in the probability density function of the variable at that value (the minimum value that can be detected). Such background values could distort both the estimation of the variable at nonsampled locations and the inference of the spatial structure of variability of the variable. Two important problems are the delineation of areas where the variable is above the detection limit and the estimation of the magnitude of the variables inside those areas. The importance of these issues in geochemical prospecting or in environmental sciences, in general related with contamination and environmental monitoring. is obvious. In this paper the authors describe the two-step procedure of indicator kriging and ordinary kriging and compare it with empirical maximum likelihood kriging. The first approach consists of using a binary indicator variable for estimating the probability of a location being above the detection limit, plus ordinary kriging conditional to the location being above the detection limit. An estimation variance, however. is not available for that estimator. Empirical maximum likelihood kriging, which was designed to deal with skew distributions, can also deal with an atom at the origin of the distribution. The method uses a Bayesian approach to kriging and gives intermittency in the form of a probability map. its estimates providing a realistic assessment of their estimation variance. The pros and cons of each method are discussed and illustrated using a large dataset of As concentration in groundwater. The results of the two methods are compared by cross-validation. (C) 2004 Elsevier Ltd. All rights reserved.
机译:用于监测地下水或土壤中的微量地球化学元素。经常存在低于仪器检测限的背景值。在监视过程中找到这些值时,会将它们分配给检测极限,在某些情况下,检测极限会在该值(可检测的最小值)的变量的概率密度函数中生成一个概率质量。这样的背景值可能会使未采样位置处的变量估计值和变量的可变性空间结构的推断都失真。两个重要的问题是确定变量超出检测极限的区域以及估计这些区域内部变量的大小。这些问题在地球化学勘探或环境科学中的重要性,通常与污染和环境监测有关。很明显。在本文中,作者描述了指标克里金法和普通克里金法的两步过程,并将其与经验最大似然克里金法进行比较。第一种方法包括使用二进制指示符变量来估计位置在检测极限以上的概率,再加上以位置在检测极限以上为条件的普通克里金法。但是,估计方差。无法用于该估算器。设计用于处理偏斜分布的经验最大似然克里金法也可以处理分布起点处的原子。该方法使用贝叶斯方法进行克里金法,并以概率图的形式给出间断性。其估算值可提供对其估算方差的现实评估。使用大量的地下水中As浓度数据集来讨论和说明每种方法的利弊。通过交叉验证比较两种方法的结果。 (C)2004 Elsevier Ltd.保留所有权利。

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