Extension of spatial information, Bayesian kriging and updating of prior variogram parameters
Extension of spatial information, Bayesian kriging and updating of prior variogram parameters
复制标题
空间信息的扩展、贝叶斯克里金法和先验变异函数参数的更新
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
D. Myers
中科院分区：
文献类型：
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作者：
H. Cui;A. Stein;D. Myers
变异函数用于描述环境变量的空间变异性。在这项研究中，表征变异函数的参数是从一个不同的，但污染严重的地区的变异函数。提出了一个程序，用于改进变差函数建模时，数据成为从感兴趣的区域。插值是通过贝叶斯形式的克里金法进行的，其中使用变异函数参数的先验分布。此过程不同于当前过程，因为避免了通常应用的变差函数最小二乘估计。这项研究说明了从荷兰的镉污染的数据，这种形式的外推相比，普通克里金。当有足够的数据（超过140个）时，普通克里金法给出了最精确的预测。当数据数量较少（即小于60）时，与普通克里金法相比，贝叶斯克里金法获得的预测更精确。这导致成本的大幅降低，而不会丢失信息。
SUMMARY Variograms are used to describe the spatial variability of environmental variables. In this study, the parameters that characterize the variogram are obtained from a variogram in a different but comparably polluted area. A procedure is presented for improving the variogram modelling when data become available from the area of interest. Interpolation is carried out by means of a Bayesian form of kriging, where prior distributions of the variogram parameters are used. This procedure differs from current procedures, since commonly applied least squares estimation for the variogram is avoided. The study is illustrated with data from a cadmium pollution in the Netherlands, where this form of extrapolation was compared with ordinary kriging. When sufficient data are available (more than 140), ordinary kriging gave the most precise predictions. When the number of data was small (i.e. less than 60), predictions obtained with Bayesian kriging were more precise as compared to those obtained with ordinary kriging. This leads to a considerable reduction of costs, without loss of information.