Anisotropy Models for Spatial Data
Anisotropy Models for Spatial Data
复制标题
空间数据的各向异性模型
DOI：
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发表时间：
2016
影响因子：
2.6
通讯作者：
Emilio Porcu
中科院分区：
文献类型：
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作者：
Denis Allard;Rachid Senoussi;Emilio Porcu
这项工作解决了为空间数据建立有用和有效的各向异性变异函数模型的问题，这些模型超越了经典的各向异性模型，如几何和区域模型。使用主不规则项的概念，在相当一般的情况下，变异函数被认为具有可能随方向变化的规则性和尺度参数。结果表明，如果正则性参数是方向的连续函数，则正则性参数必然是常数。相反，尺度参数可以随方向以连续或不连续的方式变化。为了建立一个非常大的模型类，允许超越经典的各向异性，导出了各向异性的方向混合表示。在此基础上，提出了一种用于模拟高斯各向异性过程的转弯带算法。
This work addresses the question of building useful and valid models of anisotropic variograms for spatial data that go beyond classical anisotropy models, such as the geometric and zonal ones. Using the concept of principal irregular term, variograms are considered, in a quite general setting, having regularity and scale parameters that can potentially vary with the direction. It is shown that if the regularity parameter is a continuous function of the direction, it must necessarily be constant. Instead, the scale parameter can vary in a continuous or discontinuous fashion with the direction. A directional mixture representation for anisotropies is derived, in order to build a very large class of models that allow to go beyond classical anisotropies. A turning band algorithm for the simulation of Gaussian anisotropic processes, obtained from the mixture representation, is then presented and illustrated.