Weighted Least-Squares Radon Transform
Improved attenuation of multiple energy
Optimize separation by delineating primaries and multiples based on differences in their moveout.
Shallow-water-layer demultiple method
Data-driven demultiple approaches such as surface-related multiple elimination (SRME) are negatively affected by lack of near-offset information and the limitations of first-order approximation. As a result, predictions of strong water-layer reverberations can be poor.
Our model-driven general deterministic water-layer demultiple (GDWD) method solves this challenge. It is applicable for all marine geometries, including wide-tow and undershoot data.
Move the slider to see how our GDWD and 3D GSMP general surface multiple prediction algorithm significantly improved the clarity of this North Sea data. (Data example courtesy of Statoil.)
GDWD is primarily used in shallow-water marine environments (typically <200 m deep), where it is difficult to interpolate the near-offset traces needed for the 3D GSMP algorithm. However, the technique can also be used in transition and deep water.
GDWD can accurately model complex water-bottom multiples and water-layer peg legs in a single application. Separating the recorded multiples from the seismic data is simpler and less aggressive than conventional data- and model-driven methods. Plus, GDWD can be combined with other other multiple prediction techniques and algorithms that are based on multiple periodicity and apparent velocity discrimination, such as deconvolution- and Radon-transform-based methods, to predict and attenuate all modes of surface multiples. It is often combined with 3D GSMP general surface multiple prediction algorithm, eliminating water-layer-related and other surface multiples.