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Estimation of the 3D correlation structure of an alluvial aquifer from surface-based multi-frequency ground-penetrating radar reflection data
Authors:Zhiwei Xu  James Irving  Kyle Lindsay  John Bradford  Peimin Zhu  Klaus Holliger
Affiliation:1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China;2. Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland;3. Department of Geosciences, Boise State University, Boise, Idaho, USA;4. Geophysics Department, Colorado School of Mines, Golden, Colorado, USA
Abstract:Knowledge about the stochastic nature of heterogeneity in subsurface hydraulic properties is critical for aquifer characterization and the corresponding prediction of groundwater flow and contaminant transport. Whereas the vertical correlation structure of the heterogeneity is often well constrained by borehole information, the lateral correlation structure is generally unknown because the spacing between boreholes is too large to allow for its meaningful inference. There is, however, evidence to suggest that information on the lateral correlation structure may be extracted from the correlation statistics of the subsurface reflectivity structure imaged by surface-based ground-penetrating radar measurements. To date, case studies involving this approach have been limited to 2D profiles acquired at a single antenna centre frequency in areas with limited complementary information. As a result, the practical reliability of this methodology has been difficult to assess. Here, we extend previous work to 3D and consider reflection ground-penetrating radar data acquired using two antenna centre frequencies at the extensively explored and well-constrained Boise Hydrogeophysical Research Site. We find that the results obtained using the two ground-penetrating radar frequencies are consistent with each other, as well as with information from a number of other studies at the Boise Hydrogeophysical Research Site. In addition, contrary to previous 2D work, our results indicate that the surface-based reflection ground-penetrating radar data are not only sensitive to the aspect ratio of the underlying heterogeneity, but also, albeit to a lesser extent, to the so-called Hurst number, which is a key parameter characterizing the local variability of the fine-scale structure.
Keywords:Aspect ratio  Aquifer heterogeneity  Ground-penetrating radar  Hurst number  Lateral correlation structure  Monte Carlo inversion  Water content
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