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Multiple Attenuation

Extensive portfolio to address your multiple-attenuation challenges

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Reducing multiple contamination in data is one of the greatest challenges in seismic processing, and no single approach is suitable for all scenarios. We offer an extensive portfolio of prediction and adaptive subtraction methods that address surface and internal multiples. Based on your survey challenges and processing goals, appropriate methods from this portfolio can be used to obtain the optimal solution.

Prediction methods

Currently, data-driven algorithms are the preferred prediction methods for modeling internal and surface multiples. When the prerequisites for these algorithms are difficult to satisfy, model information or transform methods are used instead.

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Multiple Attenuation Offerings

Extended Internal Multiple Prediction (XIMP)
Data-driven algorithm that ensures analysis integrity
Achieve a true-azimuth internal multiple model utilizing both seismic data and interpreted horizons.
Weighted Least-Squares Radon Transform
Improved attenuation of multiple energy
Optimize separation by delineating primaries and multiples based on differences in their moveout.
Wavefield Extrapolation Multiple Modeling
Prediction of 3D multiples independent of acquisition geometry
Employ a migrated image and associated migration velocities that is beneficial for WAZ surveys.
General Deterministic Water-Layer Demultiple
Effective attenuation of shallow-water-layer multiples
Overcome limitations of conventional demultiple approaches using a model-based method.
3D GSMP
General surface multiple prediction
Preserve double bounces and other complex primary events for removal with complementary techniques.
Inverse-Scattering Internal Multiple Prediction
Wave-theory-based, data-driven methodology
Predict internal multiples for all horizons without requiring prior subsurface information or interpretation.

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