Joint Stochastic Non-Linear Inversion of Hydrogeophysical Data for Improved Vadose Zone Characterization and Monitoring

水文地球物理数据的联合随机非线性反演以改进包气带特征和监测

• 批准号: 0439649
• 负责人: Yoram Rubin
• 金额: $ 27万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-15 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0439649&HistoricalAwards=false
• 关键词: Joint; Stochastic; Non; Linear; Inversion

0439649RubinThe vadose zone is an extremely important region to consider for environmental, agricultural and atmospheric science applications. As analyses related to vadose zone become more sophisticated, there is a mounting pressure to provide more detailed, high-resolution imaging capabilities. One of the breakthroughs in that direction has been the introduction of geophysical surveys as a key tool for hydrologic characterization. Yet, a characterization approach that can handle the nonlinearities and complexities inherent in vadose zone flow, including hysteresis and preferential flow, and geophysical processes, is still a challenge. Several promising techniques have been proposed recently, and additional investigation is needed to better understand their strength and limitations. The objective of the proposed research is to develop a stochastic inversion procedure that will allow addressing these challenges. We shall focus on the combined use of crosshole radar (GPR) and hydrogeologic data such as soil moisture and pressure head, obtained from boreholes, to provide high-resolution imaging of soil moisture dynamics, and subsequently for inverse modeling. Our hypothesis is that an inversion framework that is based on simultaneous inversion of geophysical and hydrological measurements and on non-linear modeling of the flow processes and of the geophysical surveys, improves our ability to characterize the vadose zone. The inversion of the geophysical and hydrogeological data will be carried out simultaneously using non-linear mathematical models of the flow and geophysical processes to relate between target parameters (lithology, permeability, other soil's parameters needed for modeling of the relative conductivity and water retentivity), and input data. This is motivated by the following observations. First, current methods for integrating hydrogeological and geophysical data are either sequential or iterative. The problem with sequential methods is that the error associated with the geophysical inversion is ignored, and the problem with the iterative methods is that convergence is not guaranteed, unless special conditions are met, which are difficult to evaluate. Second, several promising inversion procedures have been explored recently based on linearization (low-order approximations in terms of variability) of the flow equation. Our fully non-linear approach will explore their strength and limitations. The procedure will be stochastic in nature, with the goal of characterizing the target parameters through their multivariate spatial probability distributions. A stochastic approach is chosen because it allows to treat rationally the uncertainty due to spatial variability, data scarcity and measurement error. A Bayesian formulation will be pursued, because it allows combining prior information with site-specific measurements. Entropy-based methods (MRE: minimum Relative entropy) will be employed for determining prior probability distributions of parameters from constraints based on prior/extraneous information, with minimum subjectivity. Fuzzy neural networks will be used to develop petrophysical models. The proposed approach will be tested using a digital analogue-based synthetic model, and data from a field experiment carried out at the DOE site at Hanford. The synthetic study will test our ability to identify the soil's hydraulic parameters away from wells, and the Hanford study will assess our capability to improve predictive capabilities. The intellectual merits are primarily in the consistent treatment of uncertainty in both the geophysical and hydrological data through joint inversion (non-sequential, non-iterative), and in the use of non-linear mathematical models for the flow and geophysical processes. While ad-hoc methods for joint inversion, based on linearization of the flow equation, were found satisfactory, a scientific basis is needed for establishing their strength and limitations, and for exploring problems which are outside of current capabilities (i.e., larger variability, irregular and transient boundary conditions, and sharp fronts). There are two points we propose to consider in terms of the broader impact of the project. The first concerns geophysical inversion. It is common in the geophysical community to ignore hydrogeological constraints. While geophysical surveys are making inroads into hydrogeology, this is not true the other way around. The interpretation approach we propose can create a positive impact in that direction. The second point we view as important is the holistic approach to stochastic analysis, including all sources of error, and a rational treatment of prior information. From a narrow perspective, this approach intends to remove inconsistency in the treatment of geophysical data and to avoid subjectivity in the prior, but from a broader perspective, these ideas are meritorious for a broad class of inverse problems in and outside hydrogeology, where combined use of many sources of data, including priors, is still a challenge.

0439649Rubinthe vadose带是环境，农业和大气科学应用的极为重要的地区。随着与vadose区域相关的分析变得越来越复杂，有一个不断增加的压力可以提供更详细的高分辨率成像功能。朝这个方向的突破之一是引入地球物理调查作为水文表征的关键工具。然而，一种可以处理vadose区流中固有的非线性和复杂性的表征方法，包括滞后和优先流动以及地球物理过程，仍然是一个挑战。最近已经提出了几种有希望的技术，需要进行额外的调查以更好地了解其力量和局限性。拟议研究的目的是制定随机反演程序，以应对这些挑战。我们将重点关注孔雷达（GPR）和水文地质数据（例如土壤水分和压力头）的联合使用，从钻孔获得，以提供土壤水分动力学的高分辨率成像，然后进行反向建模。我们的假设是，基于地球物理和水文测量以及流动过程和地球物理调查的非线性模型的同时反演的反转框架提高了我们表征vadose区的能力。地球物理和水文地质数据的反转将使用流量和地球物理过程的非线性数学模型同时进行，以在目标参数（岩性，渗透性，其他土壤参数之间建模相对电导率和水隐性性）和输入数据之间相关联。这是由以下观察结果激发的。首先，整合水文地质和地球物理数据的当前方法是顺序或迭代的。顺序方法的问题在于，与地球物理反演相关的误差被忽略，迭代方法的问题是不能保证收敛，除非满足特殊条件，否则难以评估。其次，最近基于流动方程的线性化（低阶近似值）探索了几个有希望的反演过程。我们完全非线性的方法将探索它们的力量和局限性。该过程本质上将是随机的，其目的是通过其多元空间概率分布来表征目标参数。选择随机方法是因为它允许由于空间可变性，数据稀缺和测量误差而理性地处理不确定性。将追求贝叶斯的配方，因为它允许将先前的信息与特定地点的测量结合在一起。将采用基于熵的方法（MRE：最小相对熵），以根据先验/外部信息从约束中确定参数的先前概率分布，并具有最小的主观性。模糊的神经网络将用于开发岩石物理模型。提出的方法将使用基于数字模拟的合成模型以及来自Hanford的DOE站点进行的现场实验的数据进行测试。合成研究将测试我们识别土壤液压参数远离井的能力，汉福德研究将评估我们提高预测能力的能力。智力优点主要是通过关节反转（非顺序，非词语）在地球物理和水文数据中对不确定性的一致治疗，以及用于流动和地球物理过程的非线性数学模型。尽管发现基于流动方程的线性化的关节反转方法令人满意，但需要科学的基础来确定其强度和局限性，并探索与当前功能之外的问题（即较大的变异性，不规则和瞬态的边界条件以及锋利的前沿）。我们建议根据项目的更广泛影响考虑两点。第一个涉及地球物理反转。在地球物理群落中忽略水文地质限制是很常见的。尽管地球物理调查正在涉足水文地质学，但相反，情况并非如此。我们提出的解释方法可以在该方向上产生积极的影响。我们认为第二点是随机分析的整体方法，包括所有错误来源，以及对先前信息的合理处理。从狭义的角度来看，这种方法打算消除地球物理数据治疗的不一致，并避免先前的主观性，但是从更广泛的角度来看，这些想法对于在水文地质学中和外部的一系列反向问题中都具有优异的态度，其中包括许多数据来源（包括PRIEORS）的综合使用，仍然是一个挑战。