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

Duke University, Durham NC

Investigators

Abstract

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.