A perspective on 3D surface-related multiple elimination

Abstract

Surface-related multiple elimination ͑SRME͒ is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this "freedom from the subsurface," SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and trueazimuth SRME. Field data examples confirm that ͑1͒ multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and ͑2͒ on-thefly interpolation produces excellent results.

Internal multiple prediction and removal using Marchenko autofocusing and seismic interferometry

“…Examples include velocity analysis (Yilmaz, 2001;Malcolm et al, 2007) and imaging reflectors using standard linear migration (Zhu et al, 1998;Gray et al, 2001). Surface-related multiples particularly impact on seismic images resulting from marine data, and much effort has been devoted to their removal (see the review by Dragoset et al, 2010). Internal multiples strongly affect land and some marine data, but relatively fewer techniques exist to predict and remove them from reflection data.…”

Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

“…First, adaptive subtraction is usually based on minimum energy, which is not always a good assumption (Nekut and Verschuur, 1998). Second, it needs the reconstruction of missing near offsets because the data are used as a multiple predictor operator (Dragoset and Jeričević, 1998;Dragoset et al, 2010). Third, it requires dense source and receiver sampling, which often poses problems in the 3D or shallow-water-layer applications (Hargreaves, 2006;Moore and Bisley, 2006).…”

3D primary estimation by sparse inversion using the focal domain parameterization

“…All the acquisition holes present in the data volume must be filled with physical information for correct multiple prediction. One option is to use some simple interpolation method as a preprocessing step to fill all the missing traces, as commonly done with 3D SRME (Dragoset et al, 2010). However this step can lead to strong artifacts and wrongly predicted multiples if the amount of missing data is large compared with the required data volume, or if the multiple generating reflectors are shallow, as normally the interpolation quality reduces notably when the reflectors are approaching the surface.…”

“…Nowadays, 3D SRME is not necessary free of approximations, as today's 3D acquisition geometries do not provide all the measurements required for a full 3D SRME. Therefore, current implementations of 3D SRME require fast and cheap on-the-fly data interpolation (Dragoset et al, 2008;Aaron et al, 2008;Dragoset et al, 2010;Smith et al, 2011).…”