Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion
Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion
复制标题
使用贝叶斯反演表征含水层电导率的粗糙度和连通性特征的影响
DOI：
10.1016/j.jhydrol.2015.09.067
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发表时间：
2015
影响因子：
6.4
通讯作者：
Yoram Rubin
中科院分区：
文献类型：
--
作者：
Falk Heße;Heather Savoy;Carlos A. Osorio-Murillo;Jon Sege;Sabine Attinger;Yoram Rubin
由于含水层具有高度的空间变异性以及信息的稀缺性，含水层的电导率通常很难以精确的方式表示。因此，电导率通常被建模为空间随机场，由其期望值和协方差函数定义。这种协方差通常是参数化拟合模型函数的实验数据来自点测量的conductivity.In这项研究中，我们研究了两个功能，往往是很难辨别时，使用这种经典的表征方案：粗糙度和连通性。这两个特征都有一个共同的事实，即仅仅基于电导率的点测量，它们很难辨别。因此，使用额外的数据可以缓解这个问题。为此，我们使用了锚定分布方法（MAD），这是一种新的贝叶斯工具，用于空间随机场的逆特征。MAD对于所使用的数据是通用的，它具有模块化结构，并且它不假设目标变量（即测井导水率）与用于反演过程的数据（例如水头测量、抽水测试的压降或突破曲线）之间的任何正式关系。关于上述特征的表征，我们研究了几个因素的影响，如不同的数据类型上的表征process.Our的研究结果表明，粗糙度和连通性只有有限的流量预测的影响，这表明，选择这样的功能是相关性较小，如果一个主要是在流模拟感兴趣。在粗糙度的情况下，这种有限的灵敏度也被视为运输预测，如果溶质已经走过了一段距离。然而，连通性可能是这种模拟的决定性因素。
The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field, defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.In this study, we investigated the two features that are often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands to reason that the use of additional data could alleviate this problem.To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatile with respect to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process.Our findings suggest that both roughness and connectivity have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.
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DOI：
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