We present a DMO-based technique for reconstruction of densely and regularly sampled shot records from conventional marine data. The reconstruction process allows us to overcome aliasing that typically affects marine data in the cross-line direction and enables us to apply high-end processing techniques, such as 3D SRME, to conventional marine data without imposing additional acquisition requirements. A low-cost version of our reconstruction method, which is particularly suitable for use as a part of a multiple-suppression processing flow, is introduced and its advantages and limitations discussed.

An example of performing data reconstruction for a conventional marine field dataset, followed by application of 3D SRME, is presented. This field data example is used to illustrate how the choice of data reconstruction method impacts the quality of multiple suppression.


Many advanced seismic processing techniques, such as 3D surface-related multiple suppression (SRME) and 3D shot-record wave-equation migration (SRWEM), require regularly and densely sampled input data. In particular, 3D SRME requires dense sampling of both shots and receivers, while SRWEM efficiency is a function of the number of shot records in the data set. Current marine acquisition geometries do not provide the data suitable for either one of the above processing algorithms. In the case of 3D SRME, shot (and to a lesser degree, streamer) crossline spacing is too coarse for the algorithm to predict 3D multiples correctly. In the case of SRWEM, run times could be greatly reduced if multiple shot records could be combined in one "super-shot", thus reducing the total number of shot records.

We discuss a DMO-based method for transforming conventional marine data into an equivalent set of data on a finely sampled regular and dense grid including all source-to-receiver azimuths. The new data are equivalent in the sense that a migration code would accurately map a reflection event associated with a single subsurface interface in the new data to the same subsurface locations as the corresponding data in the input data set.

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