Detecting and modelling structures on the micro and the macro scales: Assessing their effects on solute transport behaviour
Detecting and modelling structures on the micro and the macro scales: Assessing their effects on solute transport behaviour
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DOI：
10.1016/j.advwatres.2017.05.007
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发表时间：
2017-09
影响因子：
4.7
通讯作者：
C. Haslauer;A. Bárdossy;E. Sudicky
中科院分区：
文献类型：
--
作者：
C. Haslauer;A. Bárdossy;E. Sudicky
本文展示了定量推理，空间分布变量的数据集分离成不同的实体，随后表征其地质统计学属性，正确。本文的主要贡献是一个基于统计的算法，匹配的手动区分结果。该算法基于实测数据，具有普遍适用性.在本文中，它被成功地应用于两个数据集的饱和导水率（K）测量在博登（加拿大）和Lauswiesen（德国）含水层。边界层在博登被成功地描绘出来，尽管它只有轻微的不均匀性，并且在划分的单元之间只有很小的统计差异。该方法被验证与更异质Lauswiesen含水层K数据集，其中边界层先前已划定。溶质运移行为的宏观和微观结构的影响进行了评估，使用数值溶质示踪剂实验。在微尺度结构中，评估K的空间依赖性的高斯和非高斯模型。异质性对宏观和微观尺度的影响进行了分析，使用数值示踪剂实验的基础上的四个方案：包括或不包括宏观尺度的结构和最佳拟合高斯或非高斯模型的空间依赖的微观结构。该文件表明，微观和宏观尺度的结构是重要的，因为在每一个四种可能的地质统计情景溶质运移行为有意义的不同。
This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated.The effects of the macro-andthe microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.