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Detecting discrete anomalies, such as cavities or tunnels, is an important application of crosshole radar tomography. However, crosshole tomographic inversion results are frequently ambiguous, showing smearing effects and inversion artifacts. These ambiguities lead to uncertainties in interpretation; hence, the size and position of anomalies can only be interpreted with limited accuracy and reliability. We present an inversion strategy for investigating discrete anomalies with crosshole radar tomography. In addition to the full traveltime data set, we use subsets of specified ray‐angle intervals for tomographic inversion. By analyzing inversion results from different ray‐angle intervals, a more accurate interpretation of anomalies is possible. The second step of our strategy is to develop a good inhomogeneous starting model from joint interpretation of the inversion results from different subsets. The third step is to invert the full data set using this new starting model and to evaluate the inversion results by analyzing the distributions of mean square traveltime residuals with respect to the ray angles. We use a synthetic model with two discrete anomalies located roughly at the same depth to demonstrate and evaluate our approach. This inversion strategy is also applied to a field data set collected to investigate karst cavities in limestone. From the inversion results of both examples, we show that horizontal smearing of anomalies can be reduced by eliminating near‐horizontal rays. A good starting model can be obtained based on the joint interpretation of the inversion results of the different subsets; it leads to a high‐resolution final image of the full data set.

Multi‐offset ground‐penetrating radar (GPR) data were collected in a coarse‐grained gravel aquifer located in a glacial delta environment within dipping foresets (at the Tettnang aquifer test site, SW Germany). We apply prestack processing techniques and normal‐moveout (NMO) velocity analysis in preparation for stacking. The estimation of propagation velocities is of considerable importance for converting time‐domain radargrams into depth‐sections and for an interpretation in terms of petrophysical properties. In our case, interval velocity determination is difficult because reflector dip angles are variable, and stacking and NMO velocities differ significantly since the antenna offset is comparable to the reflector depth. Using a synthetic two‐layer model, we systematically study possible errors in interval velocity determination. We compare the cases of decreasing velocity with depth (typical for ground‐penetrating (GPR) surveys of aquifers) and increasing velocity with depth (typical for seismic surveys). In the case of decreasing velocity with depth, the discrepancy between stacking and NMO velocity is considerable and, consequently, interval velocities calculated with the Dix equation or the more accurate 2D NMO approximation for dipping layers show unacceptably high errors. The errors are much smaller in comparable models with increasing velocity with depth. Thus, the small‐offset approximation inherent in the NMO concept is not appropriate for the first case. Consequently, it is necessary to model the true common‐midpoint (CMP) raypaths to determine realistic interval velocities from our GPR field data. Stacked GPR sections of multi‐offset data show significant improvements in imaging of reflectors and depth of investigation compared with a standard constant‐offset section. This allows a more reliable interpretation of geological structures. Simultaneous acquisition of four channels, which is possible with some commercial GPR systems, yields significantly better results than constant‐offset standard acquisition without increasing the efforts for data acquisition. Major reflectors in the GPR section can be correlated with distinct contrasts of porosity, represented by the base of open‐framework gravels and sand beds within poorly sorted gravels.

No abstract