Prestack Regularization

A crucial link in any successful imaging workflow

During seismic data acquisition, obstructions, cable feathering, environmental objectives, economic constraints, and many other factors cause the data to be sampled in a sparse and irregular fashion. These irregularities are observed in the form of variations in the fold of coverage, which can manifest itself as an acquisition footprint on prestack data or even the stacked image. If not accounted for, irregular sampling can affect data analysis and introduce noise, amplitude distortions, and even structural distortions in the final image.

This is particularly true in the prestack migration stage where Kirchhoff migration techniques have largely become the industry standard for imaging 3D data. However, the mathematical derivations of integral operators, like Kirchhoff, assume continuous wavefields. When the resulting imaging algorithms are applied to discretely sampled seismic data, the result may suffer from improper cancellation of migration impulse responses.

Note that WesternGeco Kirchhoff algorithms do not implicitly require prior regularization of source-receiver azimuths. They all fully comprehend changing source-receiver azimuths by using the true source-receiver coordinates (as opposed to bin number and nominal offset) in the computation of the operator response.

WesternGeco offers a number of techniques for processing irregularly sampled data, with the objective of interpolating missing data and equalizing the imaging process for the effects of fold variations. These techniques include Compact Fourier Interpolation, Dominant Azimuth Regularization, Azimuthal Moveout (AMO), FLEX, and Robin Hood.

It should be noted that the optimum regularization scheme is highly dependant on the nature of the input data (for example: distribution of over and under fold, noise, dip, and frequency content), the requirements of all subsequent imaging steps, and any economic constraints. There is no single solution for all cases; however, together they provide a suite of fit-for-purpose solutions for the many different applications of prestack regularization.