Stochastic assimilation of geophysical and hydrological data for the estimation of hydraulic conductivity

地球物理方法在水文学方面具有很大的潜力，因为它们能够以传统水文测量无法达到的分辨率和空间覆盖程度对地下特性进行成像。这导致了“水文地球物理学”这一新的跨学科领域最近迅速扩大。提出的研究计划的总体目标是解决当前水文地球物理研究中的一个关键问题，即如何最好地吸收不同来源的地球物理、水文和地质数据，以便在不确定的框架内估计地下水文特性。圭尔夫大学（University of Guelph）提出了两个学生项目，开始在这一领域开展工作，这两个项目的总体目标都是估计非均质冲积含水层中水力导电性的空间结构，这是地下水流动和污染物运输的主要控制因素。第一个项目将在局部（~10m）尺度上工作，并将涉及使用贝叶斯马尔可夫链-蒙特卡罗方法随机同化示踪测试浓度测量和延时电阻率数据。在这里，地球物理和水文过程模型将耦合在一起，以提供电阻率测量和水力导电性之间的联系。第二个项目将在更大（~100米）的尺度上工作，并将涉及使用多种地球物理方法和聚类分析技术，首先将地下划分为假设具有恒定水文特性的不同单元。然后，利用更大尺度的水文数据，估算带的性质及其不确定性。这两个项目的工作都将从数字“合成”研究开始，并将在2d应用程序中进行全面测试，然后再转向3d应用程序。所有获得的结果将根据其减少水文预测不确定性的能力进行评估。从长远来看，这项研究将导致更多的学生项目，包括对该领域提出的方法的测试，以及对有意义的不确定性估计至关重要的问题的详细调查，例如数据和模型结构误差的可靠评估。

Geophysical methods offer much potential in hydrology because of their ability to image subsurface properties with a resolution and degree of spatial coverage unattainable with traditional hydrological measurements. This has resulted in a recent and rapid expansion of the new and interdisciplinary field of "hydrogeophysics". The overall goal of the proposed research program is to address a key issue in current hydrogeophysical research, which is how to best assimilate diverse sources of geophysical, hydrological, and geological data in order to estimate subsurface hydrological properties within a framework of uncertainty. Two student projects are proposed to begin work in this domain at the University of Guelph, both of which have the general goal of estimating the spatial configuration of hydraulic conductivity in heterogeneous alluvial aquifers, which is the dominant control on groundwater flow and contaminant transport. The first project will work at the local (~10m) scale, and will involve the stochastic assimilation of tracer-test concentration measurements and time-lapse electrical resistivity data using a Bayesian Markov-chain-Monte-Carlo methodology. Here, the geophysical and hydrological process models will be coupled to provide a link between the resistivity measurements and hydraulic conductivity. The second project will work at a larger (~100m) scale, and will involve the use of multiple geophysical methods and cluster analysis techniques to first zone the subsurface into distinct units that are assumed to have constant hydrological properties. Then, using larger-scale hydrological data, the properties of the zones and their uncertainties will be estimated. Work on both of these projects will begin with numerical "synthetic" studies, and will be fully tested in 2-D before moving to 3-D applications. All results obtained will be evaluated in terms of their ability to reduce hydrological prediction uncertainty. Over the longer term, this research will lead to additional student projects involving testing of the proposed methodologies in the field as well as detailed investigation of issues expected to be crucial for meaningful uncertainty estimates, such as the reliable assessment of data and model structural errors.