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The vertical radar profiling (VRP) technique uses surface-to-borehole acquisition geometries comparable to vertical seismic profiling (VSP). Major differences between the two methods do arise due to the fundamentally differing nature of the velocity-depth gradients and transmitter/receiver directivities. Largely for this reason, VRP studies have so far essentially been limited to the reconstruction of velocity-depth profiles by inverting direct arrival times from single-offset VRP surveys. In this study, we investigate the potential to produce high-resolution subsurface reflection images from multi-offset VRP data. Two synthetic data sets are used to evaluate a processing strategy suitably adapted from VSP processing. Despite the fundamental differences between VRP and VSP data, we found that our processing approach is capable of reconstructing subsurface structures of comparable complexity to those routinely imaged by VSP data. Finally, we apply our processing flow to two multi-offset VRP data sets recorded at a well constrained hydrogeophysical test site in SW-Germany. The inferred VRP images are compared with high-quality surface georadar reflection images and lithological logs available at the borehole locations. We find that the VRP images are in good agreement with the surface georadar data and reliably detect the major lithological boundaries. Due to the significantly shorter ray-paths, the depth penetration of the VRP data is, however, considerably higher than that of the surface georadar data. VRP reflection images thus provide an effective means for the depth-calibration and extension of conventional surface georadar data in the vicinity of boreholes.
Inversions of an individual geophysical data set can be highly nonunique, and it is generally difficult to determine petrophysical parameters from geophysical data. We show that both issues can be addressed by adopting a statistical multiparameter approach that requires the acquisition, processing, and separate inversion of two or more types of geophysical data. To combine information contained in the physical-property models that result from inverting the individual data sets and to estimate the spatial distribution of petrophysical parameters in regions where they are known at only a few locations. we demonstrate the potential of the fuzzy c-means (FCM) clustering technique. After testing this new approach on synthetic data, we apply it to limited crosshole georadar, crosshole seismic, gamma-log, and slug-test data acquired within a shallow alluvial aquifer. The derived multiparameter model effectively outlines the major sedimentary units observed in numerous boreholes and provides plausible estimates for the spatial distributions of gamma-ray emitters and hydraulic conductivity