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

Duke University, Durham NC

Investigators

Abstract

0309626 Boadu Subsurface 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.