Prediction and Control of Urban Stormwater Quality- A Case Study

Paper by Philip B. Bedient, Jeff L. Lambert, Chris B. Amandes, and David P. Smit

Applying Reflection Tomography in the Postmigration Domain to Multifold Ground-Penetrating Radar Data

Acquisition and processing of multifold ground-penetrating radar (GPR) data enable detailed measurements of lateral velocity variability. The velocities constrain interpretation of subsurface materials and lead to significant improvement in image accuracy when coupled with prestack depth migration (PSDM). Reflection tomography in the postmigration domain was introduced in the early 1990s for velocity estimation in seismic reflection. This robust, accurate method is directly applicable in multifold GPR imaging. At a contaminated waste facility within the U.S. Department of Energy’s Hanford site in Washington, the method is used to identify significant lateral and vertical velocity heterogeneity associated with infilled waste pits. Using both the PSDM images and velocity models in interpretation, a paleochannel system that underlies the site and likely forms contaminant migration pathways is identified

Ground-Penetrating Radar Theory and Application of Thin-Bed Offset-Dependent Reflectivity

Offset-dependent reflectivity or amplitude-variationwith- offset (AVO) analysis of ground-penetrating radar (GPR) data may improve the resolution of subsurface dielectric permittivity estimates. A horizontally stratified medium has a limiting layer thickness below which thin-bed AVO analysis is necessary. For a typical GPR signal, this limit is approximately 0.75 of the characteristic wavelength of the signal. Our approach to modeling the GPR thin-bed response is a broadband, frequency-dependent computation that utilizes an analytical solution to the three-interface reflectivity and is easy to implement for either transverse electric (TE) or transverse magnetic (TM) polarizations. The AVO curves for TE and TM modes differ significantly. In some cases, constraining the interpretation using both TE and TM data is critical. In two field examples taken from contaminated-site characterization data, we find quantitative thin-bed modeling agrees with the GPR field data and available characterization data