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 | Interpolation of near offsets using multiples and prediction-error filters |  |
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The pseudo-primary-contribution gather, shown in Figure
13, does contain the water-bottom, top-of-salt,
bottom-of-salt reflections, and water-bottom multiples, caused by the
correlation between the water-bottom reflection and the first-order
multiple of the water-bottom, top-of-salt, bottom-of-salt, and
second-order water-bottom multiple, respectively. This cube is a
series of cross-correlations of single traces, with no amplitude
scaling or additional deconvolution performed before
cross-correlation. The signal-to-noise ratio in the data is lower
than that in the synthetic example. The side panel shows the same
receiver-pair correlation for multiple shots that are later summed to
produce a single output trace.
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fieldin
Figure 12. A Gulf of Mexico dataset.
The front panel is a constant-offset section, and the side panel is
a single shout, with the negative offsets predicted by
reciprocity. The image is scaled by for display purposes.
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fieldcontrib
Figure 13. Pseudo-primary
contribution gather. The front face is a constant-offset section,
while the side face contains the shots that will be summed to
produce the pseudo-primaries. The image is scaled by for
display purposes.
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Figure 14 shows the volume of pseudo-primaries
generated by summing the cross-correlations from all of the sources in
the recorded data. The pseudo-primary data are somewhat poorer than
the recorded field data in Figure 12, with the long
wavelet present in the original data strongly featured in the
pseudo-primaries. Additionally, when compared to the pseudo-primaries
in the synthetic case, the quality of the pseudo-primaries for this
example is significantly worse. Reviewing three of the points we made
about the pseudo-primaries in Sigsbee, we see that these problems are
even more pronounced in this field data example. For example, while
there were variations in the relative amplitude of the Sigsbee
pseudo-primaries, the variations in the pseudo-primaries from the
field data are much more obvious; where the water-bottom reflection is
dipping, the pseudo-primary reflection is nearly absent, and the
pseudo-primaries of the salt body have higher amplitude than anywhere
else. Second, the amplitude spectrum in the Sigsbee example was
squared as a result of the cross-correlation involved in
pseudo-primary generation. The much longer wavelet in the field data
makes this more obvious, with the side-lobes of the water-bottom
reflection appearing before the water-bottom reflection in the
recorded data. This ringing is probably associated with a water
bubble from the source. Third, the cross-talk in this field data is
also present in this pseudo-primary result. These slight smile-shaped
events have a slope the direction opposite that of the desired
pseudo-primaries, and are more obvious at farther offsets, although
the base of these smiles are at roughly zero-offset in Figure
14.
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fieldpseudo
Figure 14. Pseudo-primaries of data.
The front face is a constant-offset section, and the side face is a
shot gather. The gross structure of the original data is present,
but the squared wavelet strongly dominates the data. The image is
scaled by for display purposes.
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 | Interpolation of near offsets using multiples and prediction-error filters |  |
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Next: Interpolation of field data
Up: Field data example
Previous: Field data example
2009-04-13