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Binley, Andrew; Keery, John; Slater, Lee; Barrash, Warren and Cardiff, Mike
(2016).
DOI: https://doi.org/10.1190/GEO2015-0608.1
Abstract
Accurate estimation of the hydrological properties of near-surface aquifers is important because these properties strongly influence groundwater flow and solute transport. Laboratory-based investigations have indicated that induced polarization (IP) properties of porous media may be linked, through either semiempirical or fully mechanistic models, to hydrological properties including hydraulic conductivity. Therefore, there is a need for field assessments of the value of IP measurements in providing insights into the hydrological properties of aquifers. A cross-borehole IP survey was carried out at the Boise Hydrogeophysical Research Site (BHRS), an unconsolidated fluvial aquifer that has previously been well-studied with a variety of geophysical and hydrogeologic techniques. High-quality IP measurements were inverted, with careful consideration of the data error structure, to provide a 3D distribution of complex electrical conductivity values. The inverted distribution was further simplified using kk-means cluster analysis to divide the inverted volume into discrete zones with horizontal layering. Identified layers based on complex electrical conductivity inversions are in broad agreement with stratigraphic units identified in previous studies at the site. Although mostly subtle variations in the phase angle are recovered through inversion of field data, greater contrasts in the IP data are evident at some unit boundaries. However, in coarse-grained aquifers, such as the BHRS, the discrimination of mildly contrasting lithologic units and associated changes in hydraulic conductivity of one or two orders of magnitude are unlikely to be achieved through field IP surveys. Despite the difficulty of differentiating subtle differences between all units, overall estimates of hydraulic conductivity purely from our field IP data are typically within an order of magnitude of independently measured values.