Overview of surface and internal multiple removal strategies
Authors: Verschuur D.J. , Berkhout A.J. 
Published: October 2008

The surface-related multiple removal method has been successfully applied to several synthetic and field datasets during the last decade. The main characteristic of this wave theory based method is that the seismic data itself are used as the multi-dimensional multiple prediction operator. This avoids the necessity to bring in any subsurface information. It requires that sources and receivers be at the multiple generating interface, which is easily fulfilled for regular streamer data. For OBC data some modifications are required. With this method all possible surface-related multiples can be attenuated. This makes it attractive when several strong subsurface reflectors are present (e.g. water bottom and top of salt) that can create surface multiples which obscure the deeper (e.g. the sub-salt) structures. Furthermore, the extension to internal multiples can be made by including data driven propagation operators towards the internal multiple generating boundary.

Introduction

During the last decade, the interest in multiple elimination techniques has grown significantly. One of the reasons is that still today the problem of multiples in seismic (marine) data is not solved completely and can be one of the major hurdles to be taken in today's 3D processing flows. Currently, there is not something like the process to solve all multiple problems. The seismic processor needs a toolbox of methods from which the best one (or the best combination) can be selected to solve a specific problem.

In the past multiple attenuation was primarily achieved by making use of two different approaches: statistical predictionerror filtering and deterministic move-out filtering.

Statistical prediction-error algorithms make use of the property that a multiple tail may be well predicted from its earlier arriving primary reflection via the auto-correlation functions (Robinson, 1957). For 1-D media prediction-error filtering is very successful, particularly in the linear Radon domain (Taner, 1980). However, the more complex the medium response the less effective prediction-error filtering. In addition, lengthy prediction-error filters may distort the primary reflection events. Later the deconvolution method has been extended to a multi-dimensional one by (Taner et al., 1993), which increased the ability to follow more complex structures.

Algorithms in the second approach, deterministic move-out filtering, make use of the property that primary reflections may have a significantly smaller move-out than the interfering multiple reflections. For media with large positive velocity gradients move-out filtering may be very successful, particularly in the parabolic Radon domain (Hampson, 1986) and the hyperbolic Radon domain (Foster et al.., 1992). However, in the situation of small velocity gradients, or even velocity reversals, the difference in move out between primaries and multiples may become very small, particularly at small offsets, and move-out filtering may seriously attenuate the primary reflections as well. In addition, in the situation of complex medium responses application of the parabolic or hyperbolic Radon transform may not be meaningful any more.

In the late seventies, it was felt that particular attention should be paid to the relative strong surface-related multiples. Kennett (1979) described a 1-D forward model for surfacerelated multiples and proposed a 1-D inversion scheme.