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[1] We use complex conductivity measurements to predict the hydraulic conductivity (K) of unconsolidated materials. The samples include natural sediments and artificial sand/clay mixtures. We apply the Börner et al. [1996] model, which is based on the Kozeny-Carman equation and incorporates electrical estimates of formation factor (F) and specific surface area per unit volume-to-porosity ratio (S por ), from the real (s 0 ) and imaginary (s 00 ) conductivity components respectively. We find that K correlates with s 00 but shows no correlation with F, which we attribute to the wide range in grain size for these materials. The Börner model appears primarily dependent on the K -s 00 relation. The relationship between s 00 and S por is nonlinear and appears to depend upon material type. Further examination shows that s 00 is well correlated with effective grain size (d 10 ) and is relatively independent of the material type. We propose a simple Hazen-type equation in which the effective grain size is estimated from s 00 . This simple model provides order of magnitude estimates of K for a range of unconsolidated sediments.

The common midpoint (CMP) processing technique has been shown to be effective in improving the results of ground‐penetrating radar (GPR) profiling. When radar data are collected with the CMP multioffset geometry, stacking increases the signal‐to‐noise ratio of subsurface radar reflections and results in an improved subsurface image. An important aspect of CMP processing is normal‐moveout velocity analysis. Our objectives are to show the effect of multiple velocity analyses on the stacked radar image and particularly, to demonstrate that this velocity information can also be used to determine subsurface water content. Most GPR surveys are very limited in spatial extent and assume that within the survey range, radar velocity structure in the shallow subsurface can be adequately approximated by a single velocity function in data processing. In this study, we show that variation in radar velocity can be quite significant and that the stacked profile improves as the number of velocity analysis locations is increased. Interval velocities can be calculated from the normal moveout velocities derived in the CMP velocity analysis. With some reasonable assumptions about subsurface conditions necessary for radar propagation, interval velocity can be converted to an estimate of volumetric water content. Therefore, by collecting GPR data in the multioffset CMP geometry, not only is the radar profile improved but it also allows for an interpretation of subsurface variation in water content. We show the application of these techniques to multioffset GPR data from the Chalk River test area operated by Atomic Energy of Canada Limited.

A physiochemical model for the complex dielectric response of sedimentary rocks is used to invert broadband dielectric spectra for effective grain size distributions. The complex dielectric response of each grain within the “water‐wet” granular matrix is obtained by superimposing the polarization of the electrochemical double layer, which is assumed to surround each grain, with the complex dielectric response of the dry mineral grain. The effective complex dielectric response of the water‐wet matrix (grains and surface phase) is obtained by volume averaging over the entire distribution of particle sizes. The complex dielectric response of the total mixture (water‐wet matrix and bulk pore solution) is obtained using the Bruggeman‐Hanai‐Sen effective medium theory. Studies of Berea sandstone show that the grain size distribution, obtained by inverting the real part of the complex dielectric spectra, is similar to the grain size distribution obtained from optical images of the sample in thin section. The current model, however, does not account for surface roughness effects or the polarization of counterions over multiple grain lengths; therefore the grain size distribution obtained by dielectric spectroscopy is broader than the image‐derived distribution. The dielectric‐derived grain size distribution can be fit with two separate power laws that crossover at R ≅ 1 μm, which corresponds to a relaxation frequency of 2 kHz. The low‐frequency dielectric response (f < 2 kHz) is primarily controlled by the macroscopic grain fraction (mainly quartz grains), which has a fractal dimension of d = 1.84±0.05. The high‐frequency dielectric response (f > 2 kHz) is primarily controlled by the clay size grains and surface roughness, which has a fractal dimension of d = 2.48±0.07. At very low frequencies (f < 0.1 Hz) the dielectric response appears to be controlled by the electrochemical polarization of counterions over multiple grain lengths. A more general model should account for the effects of surface roughness and grain interactions on the dielectric response. It would also be useful to develop a simplified version of this model, perhaps similar in form to the empirically derived Cole‐Cole response, which could be more easily used to model and interpret electrical geophysical field surveys (e.g., induced‐polarization, ground‐penetrating radar, and time domain reflectometery measurements).

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