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Introduction

The last decade has seen an exponential growth in the use of 3-D seismic imaging methods. Contemporaneous with this development, imaging techniques have become more complex in the effort to account for multi-pathing in complex media and to produce true amplitude migrated pictures of the subsurface. Since multiples are not accounted for in the physical model that leads to these migration methods, they can severely and adversely affect the final migration result producing erroneous interfaces or amplitude artifacts; consequently, the multiples have to be removed from the data prior to any imaging attempt.

As pointed out by Weglein (1999), the multiple attenuation techniques may be divided into two families: (1) filtering methods which exploit the periodicity and the separability (move-out discrepancies) of the multiples and (2) the wavefield methods, where the multiples are first predicted, for example by autoconvolution of the recorded data, and then subtracted Dragoset and MacKay (1993); Verschhur et al. (1992); Weglein et al. (1997). Traditionally, filtering techniques are the method of choice for multiple processing because of their robustness and cost. However, because these techniques are mainly 1-D methods, they do not extend their multiple attenuation properties very well to higher dimensions, i.e, 2-D or 3-D. Therefore, filtering techniques have some limitations when tackling multiples in complex media. For example predictive deconvolution in the ray parameter domain fails when the water bottom is not flat Treitel et al. (1982).

In this paper I present results of a multiple attenuation technique based on the spatial predictability of both primaries and multiples. The attenuation is based on the assumption that primaries and multiples have different patterns and amplitudes. The pattern is estimated with time-space domain (t-x) multidimensional prediction-error filters (PEFs). In the first section following this introduction, I present the multiple attenuation technique. In the second section I illustrate the proposed method with a Gulf of Mexico field data example. I will show that 3-D PEFs give the best noise attenuation result. More specifically, 3-D PEFs are able to attenuate diffracted multiples better and are also less sensitive to modeling inaccuracies at short offset.


next up previous print clean
Next: Theory of multiple attenuation Up: Guitton: Pattern-based multiple attenuation Previous: Guitton: Pattern-based multiple attenuation
Stanford Exploration Project
7/8/2003