@article{BoenigerTronickeHolligeretal.2006,
  author    = {Boeniger, Urs and Tronicke, Jens and Holliger, Klaus and Becht, Andreas},
  title     = {Multi-offset vertical radar profiling for subsurface reflection imaging},
  series = {Journal of environmental \& engineering geophysics : JEEG},
  volume    = {11},
  journal   = {Journal of environmental \& engineering geophysics : JEEG},
  number    = {4},
  publisher = {EEGS},
  address   = {Denver},
  issn      = {1083-1363},
  doi       = {10.2113/JEEG11.4.289},
  pages     = {289 -- 298},
  year      = {2006},
  abstract  = {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.},
  language  = {en}
}
@article{PaascheTronickeHolligeretal.2006,
  author    = {Paasche, Hendrik and Tronicke, Jens and Holliger, Klaus and Green, Alan G. and Maurer, Hansruedi},
  title     = {Integration of diverse physical-property models : subsurface zonation and petrophysical parameter estimation based on fuzzy c-means cluster analyses},
  doi       = {10.1190/1.2192927},
  year      = {2006},
  abstract  = {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},
  language  = {en}
}
@article{BelinaDafflonTronickeetal.2009,
  author    = {Belina, Florian A. and Dafflon, Baptiste and Tronicke, Jens and Holliger, Klaus},
  title     = {Enhancing the vertical resolution of surface georadar data},
  issn      = {0926-9851},
  doi       = {10.1016/j.jappgeo.2008.08.011},
  year      = {2009},
  abstract  = {There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band- limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.},
  language  = {en}
}