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Surface-related multiples are attenuated for one sail line and one streamer of
a 3-D dataset donated by the Compagnie Générale de
Géophysique (CGG). The survey was carried out in the Gulf of
Mexico in the Green Canyon area where salt intrusions close to
the water-bottom are present. Because of the complexity of the
subsurface, a wavefield method incorporating the full
3-D volume of the data for multiple removal is necessary.
This method comprises a modeling of the multiples where
the data are used as a prediction operator and a subtraction step
where the model of the multiples is usually adaptively removed
from the data with matching filters.
The accuracy of the multiple model depends on
the source/receiver coverage at the surface. When this
coverage is not dense enough, the multiple model contains
errors that make the subtraction more difficult to
succeed. In these circumstances, one can either (1) improve the modeling
step by interpolating the missing traces, (2) improve the
subtraction step by designing methods that are less sensitive
to modeling errors, or (3) both. For this dataset, the second
option is investigated by predicting the multiples in a 2-D sense
(as opposed to 3-D) and performing the subtraction with a
pattern-based approach. Because some traces and shots
are missing for the 2-D prediction, the data are interpolated
in the inline direction with an hyperbolic radon transform with
and without sparseness constraints. The interpolation with sparseness constraint
yields the best multiple model. For the subtraction,
the pattern-based method proves to be more effective
than adaptive subtraction with matching filters at
removing surface-related multiples when the multiple model
is not accurate.

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Stanford Exploration Project

5/5/2005