Hydraulic conductivity estimation and lithological classification of an esker aquifer system using surface electrical resistivity surveys and a neural network

Abstract

Empirical and theoretical relationships between hydraulic conductivity, lithology, and electrical resistivity provide a basis for the use of electrical resistivity for aquifer characterization in unconsolidated sediments. This study demonstrates a meaningful field-scale correlation between vertically distributed hydraulic conductivity obtained from packer-based borehole hydraulic tests, and electrical resistivity obtained from surface-based geophysical surveys over the Vars-Winchester esker aquifer system, Ontario, Canada. Electrical resistivity alone has order-of-magnitude predictive capacity for hydraulic conductivity, but is insufficient to reliably discriminate between aquifer and aquitard lithology. An alternative methodology is developed that takes advantage of the observed correlation between hydraulic conductivity and elevation, and the separability of lithology in terms of elevation. Electrical resistivity and elevation are combined as predictor variables for hydraulic conductivity using both multiple linear regression and nonlinear neural network regression, and for neural network classification of lithology. Neural network regression results in prediction accuracy for log-transformed hydraulic conductivity of 0.38–0.52 with clear definition of vertical and lateral heterogeneity. Classification accuracy for lithology is 83–84% with high probability of discrimination between the unconsolidated aquifer and aquitard sediments, and lower probability identification of the bedrock surface due to fewer samples at depth and limited penetration depth of the resistivity survey.