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We present cross-hole P- and S-wave seismic experiments that have been performed along a similar to 100 m long transect for the detailed characterization of a contaminated sedimentary site (Bitterfeld research test site, Germany). We invert the corresponding first break arrival times for the P- and S-wave velocity structure and compare two different strategies to interpret these models in terms of pertinent lithological and geotechnical parameter variations. The first (common) approach is based on directly translating the tomographic velocity models into the parameters of interest (e.g., elastic moduli). The second (zonal) approach first reduces the tomographic parameter information to a limited number of characteristic velocity combinations via k-means cluster analysis. Then, for each zone (cluster) further parameters including uncertainties can be estimated. In the presented case study, Our results indicate that the zonal approach provides an effective means for the integrated interpretation of different co-located data.
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.