Summary

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.