Seismic migration of blended shot records with surface-related multiple scattering

This paper focuses on the concept of using blended data and multiple scattering directly in the migration process, meaning that the blended input data for the proposed migration algorithm includes blended surface-related multiples. It also means that both primary and multiple scattering contribute to the seismic image of the subsurface....

A perspective on 3D surface-related multiple elimination

Surface-related multiple elimination (SRME) is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on...

Estimation of primaries by sparse inversion from passive seismic data

For passive seismic data, surface multiples are used to obtain an estimate of the subsurface responses, usually by a crosscorrelation process. This crosscorrelation process relies on the assumption that the surface has been uniformly illuminated by subsurface sources in terms of incident angles and strengths. If this is not the case, the...

Estimation of primaries and near-offset reconstruction by sparse inversion: Marine data applications

ost wave-equation-based multiple removal algorithms are based on prediction and subtraction of multiples. Especially for shallow water, the prediction strongly relies on a correct interpolation of the missing near offsets. The subtraction of predicted multiples from the data can easily lead to the distortion of primaries if primaries and...

Assessing estimated velocity-depth models: Finding error bars in tomographic inversion

In conventional migration velocity analysis methods, a velocity model is estimated that results in flattened events in common-image gathers. However, after this process, no information is available on the accuracy of this velocity model. A statistical analysis of velocity-model parameters is very difficult because of the integrated nature of the...

Adaptive curvelet-domain primary-multiple separation

In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function...

Focal transformation, an imaging concept for signal restoration and noise removal

Interpolation of data beyond aliasing limits and removal of noise that occurs within the seismic bandwidth are still important problems in seismic processing. The focal transform is introduced as a promising tool in data interpolation and noise removal, allowing the incorporation of macroinformation about the involved wavefields. From a physical...

Imaging of multiple reflections

Current multiple-removal algorithms in seismic processing use either differential moveout or predictability. If the differential moveout between primaries and multiples is small, prediction is the only option available. In the last decade, multidimensional prediction-error filtering by weighted convolution, such as surface-related multiple...