Physical and Chemical Heterogeneity of Aquifers: A Quantitative Geological Investigation

小行星9526795 地下溶质运动的预测是水文地质学家面临的主要挑战之一。 人们普遍认识到，含水层渗透性的空间变化是控制污染物羽流演变的主要因素之一。 此外，最近的研究表明，含水层化学性质的空间变化也可能影响溶质运动。 一个非常有前途的方法来量化地下变异性和相关的污染物传输是地质统计学的方法。 地质统计学或随机模型用空间相关的随机场表示含水层特性。 近年来，文献中提出了各种各样的统计模型来表示渗透率的空间变化。其中一些模型也被应用于化学性质的空间变化。 一般来说，每个模型都是基于不同的方法，用于数学表示或计算生成空间相关的随机变量，并且每个模型都表现出外观的总体差异。 本研究探讨了各种统计模型与沉积地质过程的关系，以及如何使用地质信息来选择和参数化最合适的模型。 从以前的工作结果表明，河流边界面的沉积学特征和地质统计学特性的模态和空间相关性之间存在着一种可量化的关系。 河流边界表面在本质上是分层的，并且已经观察到，一般来说，边界表面的阶数越高，表面将具有不同平均渗透率的区域分开的概率越高。 平均渗透率不同的区域又是非均质统计模型形态和空间相关结构的主要控制因素。 关于含水层化学性质的空间变化，关于变化的空间模式的实地数据很少。 这项研究将获得关于化学不均匀性的性质及其与沉积地质过程的关系的重要数据。 研究的主要目的是：（1）实证研究渗透率相关结构与分层边界面空间分布的关系，（2）研究化学性质空间变异与地质特征的关系，（3）研究渗透率相关结构与分层边界面空间分布的关系，（4）研究化学性质空间变异与地质特征的关系。（3）建立了一套沉积环境信息的编码算法，用于物理化学非均质性的模拟。 第一阶段将调查新英格兰砾石坑中暴露的沉积物的渗透性和化学性质（矿物学、颗粒涂层、表面积、孔隙度和阳离子交换能力）的空间变化。 野外工作的结果将用于开发模拟含水层非均质性的算法。 预计该算法将分别使用指标形式主义和贝叶斯统计来处理不同尺度的异质性。 每个地质环境都是独特的，任何特定的露头研究都不能直接转移到其他地点。 然而，如果物理和化学非均质性可以与一般的地质性质和沉积学特征，这项研究的结果将使更多的可移植性之间的不同地质背景。

9526795 Davis One of the major challenges facing hydrogeologists today is the prediction of solute movement in the subsurface. It has been generally recognized that the spatial variation of aquifer permeability is one of the dominant controls on the evolution of a contaminant plume. In addition, recent studies suggest that the spatial variation in aquifer chemical properties may also influence solute movement. One very promising approach to quantifying subsurface variability and the associated contaminant transport is the geostatistical approach. Geostatistical, or stochastic, models represent aquifer properties with spatially correlated random field. In recent years, a wide variety of statistical models have been proposed in the literature to represent the spatial variation of permeability. Some of these models have also been applied to the spatial variation of chemical properties. In general, each model is based on a different method for mathematically representing, or computationally generating, spatially correlated random variables and each exhibits overall differences in appearance. This research investigates how the various statistical models relate to the geological processes of deposition and how geologic information can be used to select and parameterize the most appropriate model. Results from previous work indicate that a quantifiable relationship exists between the sedimentological features of fluvial bounding surfaces and the geostatistical properties of modality and spatial correlation. Fluvial bounding surfaces are hierarchical in nature and it has been observed that, in general, the higher the order of the bounding surface, the higher the probability that the surface separates regions with different mean permeability. The regions of different mean permeability in turn are the dominant control on modality and spatial correlation structure of the statistical models of heterogeneity. With regard to the spatial variation of aquifer chemical properties, very little fi eld data on the spatial patterns of variability. This research will obtain important data on the nature of chemical heterogeneity and its relationship to the geological processes of deposition. The primary objectives of the proposed research are to: 1) empirically investigate the relationship between the correlation structure of permeability and the spatial distribution of hierarchical bounding surfaces; 2) investigate the relationship between the spatial variation of chemical properties and geologic features; and 3) develop an algorithm for coding sedimentological information of depositional environment for the simulation of the physical and chemical heterogeneity. The first phase will investigate the spatial variation of both permeability and chemical properties (mineralogy, grain coatings, surface area, porosity, and cation-exchange capacity) of deposits exposed in New England gravel pits. The results of the field work will be used to develop an algorithm of simulating aquifer heterogeneity. It is anticipated that the algorithm will treat different scales of heterogeneity separately using the indicator formalism and Bayesian statistics. Each geologic setting is unique and any particular outcrop study will not be directly transportable to other sites. However, if the physical and chemical heterogeneity can be related to general geologic properties and sedimentological features, the results of this research will enable much more portability between different geologic settings.