A comparison of three multiple-attenuation methods for a Gulf of Mexico dataset

Three multiple attenuation techniques are tested on a Gulf of Mexico dataset. These methods are (1) a hyperbolic Radon transform followed by a mute (2), the Delft approach and (3), a pattern-based technique. The Radon transform separates multiples and primaries according to their moveout. The Delft approach models the multiples and subtracts them by estimating adaptive filters. The pattern-based method uses the multiple model from the Delft approach to extract and separate multiples from the primaries according to their multivariate spectra. Because of the complex geology and the modeling uncertainties introduced by 3-D effects and the acquisition geometry, the Radon transform and the Delft approach do not perform as well as the pattern-based method. In addition, the pattern-based method works significantly better when higher dimension filters are utilized: diffracted multiples are well attenuated while preserving the primaries.