Imaging Subsurface Hydraulic Properties From Spectral Electrical Tomographic Images: Artificial Neural Network Approach

地球地下的地质环境，水文地质和地球物理研究，包括含水层特性，污染物的运输和污染物修复策略的设计，需要准确可靠的预测水力特性的空间变异性。 本研究的主要目标是开发一种综合方法，它结合了基于物理化学的模型开发，实验室测量，解释和成像工具，使用人工神经网络，预测水力传导率和孔隙度图像的地球的地下频率依赖电阻率图像。 将开发基于物理化学原理的土壤频率相关电阻率模型，其中包括水力和岩石物理参数作为主要变量。 将设计实验室实验来测量频率相关电阻率以及来自不同地质环境的100多个硅样品的岩石物理和水力性质，旨在获得土壤质地和结构性质的足够可变性。 这些测量将允许一个全面的调查如何光谱电响应的土壤是由他们的岩石物理和水力特性的影响。 将在实验室测量土壤样品的导水率、孔隙率、密度、含水量和颗粒大小分布，以充分确定土壤样品的特征。 人工神经网络（ANN）的能力，以适应，概括和识别变量之间的关系的非线性，将探讨确定土壤的光谱电响应和它们的水力特性之间的函数关系。 将在选定的地质地点，根据已知的水力特性信息，以特定频率进行电阻率和相位的现场测量。 提出了一种使用正则化牛顿法的反演方法，并将其用于获得地下电特性的频率扫描层析图像。 地下图像的水力特性将预测从现场测量的断层图像的电气propertiesusing转换开发的基础上人工神经网络的功能关系。 将评估由于噪声和模型参数错误表示以及训练方法的效率低下而导致的拟议预测方法的不确定性。 从现场测量的水力特性的方法的输出将进行比较，现有的地面实况信息。

Geo-environmental, hydrogeological and geophysical studies of the earth's subsurface involving aquifer characterization, transport of contaminants and design of contaminant remediation strategies demand accurate and reliable prediction of the spatial variablity of hydraulic properties. The primary goal of this research is to develop an integrated approach, which incorporates physico-chemically based model developments, laboratory measurements, interpretational and imaging tools using artificial neural networks, to predict hydraulic conductivity and porosity images of the earth's subsurface from frequency dependent resistivity images. Models describing the frequency dependent resistivity of soils based on physico-chemical principles will be developed that will include hydraulic and petrophysical parameters as primary variables. Laboratory experiments will be designed to measure frequency dependent resistivity as well as the petrophysical and hydraulic properties of over 100 sil samples fromdifferent geological environments aiming to obtain adequate variablity in soil textural andstructural properties. These measurements will allow a comprehensive investigation of how spectral electrical responses of soils are influenced by their petrophysical and hydraulic properties. The soil samples will be fully characterized by measuring their hydraulic conductivity, porosity, density, moisture content and particle size distribution in the laboratory. The capabilities of artificial neural networks (ANN) to adapt, generalize and identify non-linearities in relations among variables, will be explored to determine the functional relationships between the spectral electrical response of soils and their hydraulic properties. Fieldmeasurements of resistivity and phase at specified frequencies will be conducted at selected geologic sites with known information on the hydraulic properties. An inversion procedure using a regularized Newton's method is proposed and will be utilized to obtain frequency - scanned tomographic images of electrical properties of the subsurface. Subsurface images of hydraulic properties will be predicted from the field measured tomographic images of electrical propertiesusing transformations to be developed based on ANN functional relationships. Uncertainties in the proposed prediction method due to noise and model parameter misrepresentations and inefficiencies in the training method will be assessed. The outputs of the method from field measured hydraulic properties will be compared to that of existing ground truth information.