SYNTHETIC MODEL AND REALITY

The method has been applied on a 1D elastic model, generated by a program that uses Haskel-Thompson propagation matrices. Figure  shows the original synthetic seismogram and the model, which contains a total of seven layers overlaid by water, each of them with constant density and Poisson's ratio. The group interval is 50 m with 48 channels and a maximum offset of 2650 m.

To facilitate the identification of each of the several superposed events, four other auxiliary synthetics were also generated. Two of them are simple ray-tracing synthetics for PSPP-PPSP primary arrivals (Figure a ) and for PSSP primary arrivals (Figure b ); the sea-bottom reflection is also included in both seismograms. The other two correspond to an acoustical equivalent seismogram using the same P wave velocities as the elastic model (Figure a ), and a seismogram free of ``water-layer multiples" (Figure b ), where air was replaced by water.

All of the three most basic converted modes (PPSP, PPSP and PSPP) are present on Figure b (without the interference of multiples), but not on Figure a; for example, the events that reach the farthest offset of Figure b at 2.4s (PSPP) and 3.0s (PSSP) or the nearest offset at 0.9s (PSPP), 1.05(PSSP) and 1.8(PSPP) and are all absent on Figure a and hard to distinguish on Figure b .

A comparison between the model before and after the application of the multiples-suppression algorithm is showed in Figures a and b. It is evident that the method fails to remove the multiples in the near traces. The reasons for that result are discussed along with the description of the method in Appendix A. Whereas the suppression's efficiency at the small offsets is not relevant for the present goals, the algorithm's performance at large offsets is crucial for the isolation of the PSSP wavefield. Several converted-waves arrivals are more evident after the multiple's attenuation, such as the reflections that cross the farthest offset at 3.0s and 4.05s on section b of Figures  and  and are absent on section a of both figures. Meanwhile, some further refinement is still required for the multiples-removal process at large offsets, as it can be seen by the presence of a PPSP water-bottom multiple at 2.7s in the last traces of the deconvolved data.

When a constant horizontal-slowness filter is used to perform the S wavefield separation, the result exhibits an ``artificial appearance" due to the restricted slowness range accepted by it. In contrast, the splitting method based on the separation of the data into different Snell domains preserves a most suitable range of stepouts in the S wavefield section. Figures a and b provide a good comparison between the two methods. All the events are doubtless more clearly discernible in b than in a . A correlation between Figure  (which contains the expected positions of the converted primary reflections) and Figure b (which corresponds to the separated S field) shows that, although most of the desired events are present, some multiples associated with converted waves are also visible, such as the ones that intercept the last trace at 3.5s and 4.5s. These events correspond to multiples that were not satisfactorily eliminated in the suppression process.

Since multiples and primaries have the same horizontal slownesses, the multiples of P waves will be also eliminated during the filtering process when a constant slowness cutoff is used. However, when the data is divided into different ranges of horizontal slowness so that a variable filter can be applied, the multiples and primaries inside a region will have different horizontal slownesses and the contamination with multiples will became critical.

An overall idea of the differences between the two methods of mode separation and the contribution of the multiples-suppression can be achieved if we contrast the velocity analysis panels regarding each step (Figure a -d ). The panel in a refers to the original model while the velocity analysis in b corresponds to the output of the multiples-suppression process. The differences of interest appear only on the shallow events, like the one with velocity 1350m/s at 0.75s that is present only on b or the one with velocity 1300m/s at 2.2s that is stronger in b . In addition, the velocity panel corresponding to the constant-slowness splitting is displayed in c while the last panel (d ) refers to the variable-slowness filtering applied over different Snell zones. It is important to notice that whereas the shallow events are easy to identify in c and most of the energy associated with multiples has been suppressed, an undesirable trend corresponding to aliased energy strongly contaminates the whole panel. The use of a variable ray parameter filter, however, provides a clean panel with a definite trend of converted-waves' stacking velocities.

The same procedures were applied on data recorded by GECO in Barents Sea (offshore Norway). The data is composed of 48 channels with a group interval of 50 m and maximum offset of 2650 m. The water depth is close to 300 m and the ocean floor is ``semi-hard" (P velocity around 1850 m/s). A shot gather of this data is shown in Figure a and the same gather, after multiples-suppression and separation of the converted wavefield by the variable slowness algorithm, is displayed in b . The velocity panels corresponding to the two seismograms are shown in c and d . Although a nonambiguous interpretation of the velocity trend is not feasible, it is possible to delimit with reasonable confidence the range of acceptable stacking velocities for the converted waves.

The application of the method in a more appropriate dataset is still required, because the poor quality of the present data and the inadequacy of the survey parameters (maximum offset and group interval) strongly restrict the resolution of any prestack procedure and compromise a critical evaluation of its efficiency.