Hydrological Characterization and Petrophysical Analysis of Saturated and Unsaturated Soils from Electrical Response Measurements

[309626]为了研究含水层特征、污染物运移和污染物修复策略的设计，无论是在饱和区还是不饱和区进行地下表征，都需要对岩石物理和水力特性进行准确可靠的预测。该研究的主要目标是开发一种综合方法，该方法结合了基于物理化学的模型开发、实验室测量和使用人工神经网络的解释工具，通过频率相关电阻率测量来预测地球地下的饱和或不饱和水力导电性、孔隙度和水分含量。将开发基于物理化学原理的描述频率相关土壤电阻率的模型，其中包括水力和岩石物理参数作为主要变量。在前两年，将设计实验室实验，测量来自不同地质环境的100多个土壤样品的频率相关电阻率以及岩石物理和水力特性。土壤样品将在实验室中通过测量其水力导率、孔隙度、密度、含水量和粒度分布（粘土和有机质含量）来充分表征。可接受的pedo传递函数将用于提供有关饱和或非饱和水力导电性的有用信息。在最后一年，将探索人工神经网络（ANN）适应、推广和识别复杂变量和耦合变量之间非线性关系的能力，以确定土壤的谱电响应与其水力和岩石物理性质之间的函数关系。本文将评估所提出的预测方法中由于噪声和模型参数错误表征以及训练方法效率低下而导致的预测变量的不确定性。

0309626BoaduSubsurface characterization whether in the saturated or unsaturated zone for the purposes of aquifer characterization, transport of contaminants and design of contaminant remediation strategies, demand accurate and reliable prediction of the petrophysical and hydraulic properties. The primary goal of the proposed research is to develop an integrated approach, which incorporates physico-chemically based model developments, laboratory measurements and interpretational tools using artificial neural networks, to predict saturated or unsaturated hydraulic conductivity, porosity and moisture content of the earth's subsurface from frequency dependent resistivity measurements. 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. In the first two years, laboratory experiments will be designed to measure frequency dependent resistivity as well as the petrophysical and hydraulic properties of over 100 soil samples from different geological environments. The soil samples will be fully characterized by measuring their hydraulic conductivity, porosity, density, moisture content and particle size distribution (clay and organic matter content) in the laboratory. Acceptable Pedo-transfer functions will be used to provide useful information on both saturated or unsaturated hydraulic conductivities. In the final year, the capabilities of artificial neural networks (ANN) to adapt, generalize and identify non-linearities in relationships among complex and coupled variables, will be explored to determine the functional relationships between the spectral electrical response of soils and their hydraulic and petrophysical properties. Uncertainties in the predicted variables in the proposed prediction method due to noise and model parameter misrepresentations and inefficiencies in the training method will be assessed.