[1] Developing a predictive understanding of subsurface contaminant plume evolution and natural attenuation capacity is hindered by the inability to tractably characterize controlling reactive transport properties over field-relevant scales. Here we explore a concept of reactive facies, which is based on the hypothesis that subsurface units exist that have unique distributions of properties that influence reactive transport. We further hypothesize that geophysical methods can be used to identify and spatially distribute reactive facies and their associated parameters. We test the reactive facies concept at a U.S. Department of Energy uranium-contaminated groundwater site, where we have analyzed the relationships between laboratory and field (including radar and seismic tomographic) data sets. Our analysis suggests that there are two reactive facies that have unique distributions of mineralogy, texture, hydraulic conductivity, and geophysical attributes. We use these correlations within a Bayesian framework to integrate the dense geophysical data sets with the sparse corebased measurements. This yields high-resolution (0.25 m  0.25 m) estimates of reactive facies and their associated properties and uncertainties along the 2-D tomographic transects.Comparison with colocated samples shows that the estimated properties fall within 95% uncertainty bounds. To illustrate the value of reactive facies characterization approach, we used the geophysically estimated properties to parameterize reactive transport models, which were then used to simulate migration of an acidic-U plume through the domain. Modeling results suggest that each identified reactive facies exerts a unique control on plume evolution, highlighting the usefulness of the reactive facies concept for spatially distributing properties that control reactive transport over field-relevant scales.Citation: Sassen, D. S., S. S. Hubbard, S. A. Bea, J. Chen, N. Spycher, and M. E. Denham (2012), Reactive facies: An approach for parameterizing field-scale reactive transport models using geophysical methods, Water Resour. Res., 48, W10526,

A stochastic model is developed to integrate multiscale geophysical and point data sets for characterizing coupled subsurface physiochemical properties over plume-relevant scales, which is desired for parameterizing reactive transport models. We utilize the concept of reactive facies, which is based on the hypothesis that subsurface units can be identified that have distinct reactive-transport-property distributions. To estimate and spatially distribute reactive facies and their associated properties over plumerelevant scales, we need to (1) document the physiochemical controls on plume behavior and the correspondence between geochemical, hydrogeological, and geophysical measurements; and (2) integrate multisource, multiscale data sets in a consistent manner. To tackle these cross-scale challenges, we develop a hierarchical Bayesian model to jointly invert various wellbore and geophysical data sets that have different resolutions and spatial coverage. We use Markov-chain Monte-Carlo sampling methods to draw many samples from the joint posterior distribution and subsequently estimate the marginal posterior distribution of reactive-facies field and their associated reactive transport properties. Synthetic studies demonstrate that our method can successfully integrate different types of data sets. We tested the framework using the data sets collected at the uranium-contaminated Savannah River Site F-Area, including wellbore lithology, cone penetrometer testing, and crosshole and surface seismic data. Results show that the method can estimate the spatial distribution of reactive facies and their associated reactive-transport properties along a 300 m plume centerline traverse with high resolution (1.2 m by 0.305 m).

Ground-penetrating radar (GPR) can detect and describe fractures to help us characterize fractured rock formations. A fracture alters the incident waveform, or wave shape, of a GPR signal through constructive and destructive interference, depending on the aperture, fill, and orientation of the fracture. Because the electromagnetic (EM) waves of GPR are vectorial, features exhibiting strong directionality can change the state of polarization of the incident field. GPR methods that focus on changes in waveform or polarization can improve detection and discrimination of fractures within rock bodies. An algorithm based on coherency, a seismic attribute that delineates discontinuities in wavelet shape, is developed for polarimetric GPR. It uses the largest eigenvalue of the time-domain scattering matrix when calculating coherence. This algorithm is sensitive to wave shape and is unbiased by the polarization of GPR antennas. Polarimetric coherency works better than scalar coherency in removing the effects of polarization on field data collected from a fractured limestone plot used for hydrologic experimentation. Another method, for time-domain full-waveform inversion of transmission data, quantitatively determines fracture aperture and EM properties of fill, based on a thin-layer model. Inversion results from field data show consistency with the location of fractures from reflection data. These two methods offer better fracture-detection capability and quantitative information on fracture aperture, dielectric permittivity, and electrical conductivity of the fill than traditional GPR imaging and scalar-coherency attributes.

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