## 4 Discussion  
If we compare Figures 7 and 13b, it is obvious that the automatic CMP stack used as the first step of the pragmatic search strategy is far less successful than the conventional CMP stack methodology. For the making of this judgment, we assume a minor influence of the DMO procedure that was applied in Figure 13b, but not in Figure 14. In general, one can conclude from Figure 7 that the automatic CMP stack was only successful in imaging event \(\alpha\), and mainly on the left half of the section. Interestingly, the relatively strong reflection imaged above event \(\alpha\), around CMP 140, may be connected to event \(\gamma\), which is not present in the original CMP stack results. The general picture gets a little bit clearer in Figure 8, where the CRS operator was used for stacking based on the parameter values estimated by the cascaded search strategy. The improvement of the S/N ratio is notable, which helps to uncover some parts of event \(\alpha\) on the right side of the section. Particularly there, on the right side above event \(\alpha\), traces of event \(\gamma\) emerge from the noise. The enhancement obtained from a subsequent local three-parameter optimization is clearly visible in Figure 9. Reflections become slightly more pronounced, and the traces of shallow events show up above event \(\alpha\). Going on to Figure 10, the improved lateral event-continuity resulting from the three iterations of event-consistent smoothing of the stacking parameters followed by local three-parameter optimization is notable. In Figure 11, where the CRS stack result using the hybrid diffraction/reflection search algorithm is displayed, the picture changes relatively drastically. Event \(\gamma\) becomes quite clearly observable over the whole section, except for the area between CMP 100 and 130. In addition, event \(\alpha\) can be traced now through the whole section. Even the gap around CMP 240 is covered, but is still not as well  
Figure 16: CRS NMO velocities obtained from hybrid diffraction/reflection parameter optimization. Figure 17: CRS NMO velocities obtained from full simultaneous three-parameter search. defined as the rest of event \(\alpha\). Finally, the stack section displayed in Figure 12, resulting from the full simultaneous three-parameter search, is the best image we obtained for this data, but outperforms the stack result displayed in Figure 11, only in tiny details. In both Figures 11 and 12, an ultra-shallow horizontal event, which might belong to seismic unit A, can be seen throughout the whole section except in the area between CMPs from 140 to 160. This gap in the continuity of the event is probably due to the bad quality of seismic signals, which not even the simultaneous three-parameter CRS stack was able to improve. However, as mentioned in the original publication of [46], these very early traveltimes are not very reliable since the usable stacking aperture in offset direction is extremly narrow. Furthermore, comparing Figure 12 (or even Figure 11) and Figure 13a, it is obvious that, relative to the NMO/DMO stack, the CRS stack is better able to image the internal layering of unit B, at least beyond CMP 160. In the first part of the section the internal structures of unit B are not as evident, probably due to a lack of data resolution and/or low data quality. Coming to the NMO velocities, we will start with Figure 14, which displays NMO velocities calculated from CRS parameters estimated using the cascaded search strategy. Here, we have first to state that, in this section, the deep blue color on the upper right edge is used as no-value flag, while the deep brownish color in the rest of the section can be seen as the no-useful-value flag, since the coherence analysis tends to favor very high NMO velocities, i.e., a flat traveltime assumption, in the absence of detectable events. Having said this, we can state that, in Figure 14, well defined velocities are mainly available for event \(\alpha\) and predominantly on the left side of the section.