Time- and offset-domain internal multiple prediction with nonstationary parameters

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V105-V116 ◽  
Author(s):  
Kristopher A. Innanen
Keyword(s):  
Plane Wave ◽  
Inverse Scattering ◽  
Time Domain ◽  
Quantitative Interpretation ◽  
Seismic Processing ◽  
Priority Area ◽  
Multiple Attenuation ◽  
Multiple Prediction ◽  
The Time Domain ◽  
Prediction Parameters

Practical internal multiple prediction and removal is a high-priority area of research in seismic processing technology. Its significance increases in plays in which data are complex and sophisticated quantitative interpretation methods are apt to be applied. When the medium is unknown and/or complex, and moveout-based primary/multiple discrimination is not possible, inverse scattering-based internal multiple attenuation is the method of choice. However, challenges remain for its application in certain environments. For instance, when generators are widely distributed and are separated in space by a range of distances, optimum prediction parameters such as [Formula: see text] (which limits the proximity of events combined in the prediction) are difficult to determine. In some cases, we find that no stationary value of [Formula: see text] can optimally predict all multiples without introducing damaging artifacts. A reformulation and implementation in the time domain permits time nonstationarity to be enforced on [Formula: see text], after which a range of possible data- and geology-driven criteria for selecting an [Formula: see text] schedule can be analyzed. The 1D and 1.5D versions of the time-nonstationary algorithm are easily derived and can be shown to add a new element of precision to prediction in challenging environments. Merging these ideas with multidimensional plane-wave domain versions of the algorithm provides 2D/3D extensions.

A plane-wave formulation and numerical analysis of elastic multicomponent inverse scattering series internal multiple prediction

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. V255-V269 ◽  
Author(s):  
Jian Sun ◽  
Kristopher A. Innanen
Keyword(s):  
Numerical Analysis ◽  
Plane Wave ◽  
Inverse Scattering ◽  
Simulated Data ◽  
Velocity Model ◽  
Prediction Algorithm ◽  
Inverse Scattering Series ◽  
Elastic Data ◽  
Reference Medium ◽  
Multiple Prediction

Internal multiple prediction and removal is a critical component of seismic data processing prior to imaging, inversion, and quantitative interpretation. Inverse scattering series methods predict multiples without identification of generators, and without requiring a velocity model. Land environments present several challenges to the inverse scattering series prediction process. This is particularly true for algorithm versions that explicitly account for elastic conversions and incorporate multicomponent data. The theory for elastic reference medium inverse scattering series internal multiple prediction was introduced several decades ago, but no numerical analysis or practical discussion of how to prepare data for it currently exists. We have focused our efforts on addressing this gap. We extend the theory from 2D to 3D, analyze the properties of the input data required by the existing algorithm, and, motivated by earlier research results, reformulate the algorithm in the plane-wave domain. The success of the prediction process relies on the ordering of events in either pseudodepth or vertical traveltime being the same as the ordering of reflecting interfaces in true depth. In elastic-multicomponent cases, it is difficult to ensure that this holds true because the events to be combined may have undergone multiple conversions as they were created. Several variants of the elastic-multicomponent prediction algorithm are introduced and examined for their tendency to violate ordering requirements (and create artifacts). A plane-wave domain prediction, based on elastic data that have been prepared (1) using variable, “best-fit” velocities as reference velocities, and (2) with an analytically determined vertical traveltime stretching formula, is identified as being optimal in the sense of generating artifact-free predictions with relatively small values of the search parameter [Formula: see text], while remaining fully data driven. These analyses are confirmed with simulated data from a layered model; these are the first numerical examples of elastic-multicomponent inverse scattering series internal multiple prediction.

3D prediction of surface-related and interbed multiples

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. V1-V6 ◽  
Author(s):  
Moshe Reshef ◽  
Shahar Arad ◽  
Evgeny Landa
Keyword(s):  
Data Processing ◽  
Time Domain ◽  
Final Section ◽  
Real Data ◽  
Computational Procedure ◽  
Detailed Knowledge ◽  
Geological Model ◽  
Multiple Attenuation ◽  
The Time Domain ◽  
Kinematic Properties

Multiple attenuation during data processing does not guarantee a multiple-free final section. Multiple identification plays an important role in seismic interpretation. A target-oriented method for predicting 3D multiples on stacked or migrated cubes in the time domain is presented. The method does not require detailed knowledge of the subsurface geological model or access to prestack data and is valid for both surface-related and interbed multiples. The computational procedure is based on kinematic properties of the data and uses Fermat's principle to define the multiples. Since no prestack data are required, the method can calculate 3D multiples even when only multi-2D survey data are available. The accuracy and possible use of the method are demonstrated on synthetic and real data examples.

A sampling method for inverse scattering in the time domain

2010 ◽  
Vol 26 (8) ◽  
pp. 085001 ◽  
Author(s):  
Q Chen ◽  
H Haddar ◽  
A Lechleiter ◽  
P Monk
Keyword(s):  
Inverse Scattering ◽  
Time Domain ◽  
Sampling Method ◽  
The Time Domain

Internal multiple prediction using inverse-scattering series with sparsity promotion — Part 1: Background, analysis, and synthetic modeling

Geophysics ◽  
2021 ◽  
pp. 1-94
Author(s):  
Ole Edvard Aaker ◽  
Adriana Citlali Ramírez ◽  
Emin Sadikhov
Keyword(s):  
Plane Wave ◽  
Inverse Scattering ◽  
Seismic Data ◽  
Data Completeness ◽  
Inverse Scattering Series ◽  
Recent Developments ◽  
Internal Multiples ◽  
Multiple Prediction ◽  
Synthetic Modeling ◽  
The Way

The presence of internal multiples in seismic data can lead to artefacts in subsurface images ob-tained by conventional migration algorithms. This problem can be ameliorated by removing themultiples prior to migration, if they can be reliably estimated. Recent developments have renewedinterest in the plane wave domain formulations of the inverse scattering series (ISS) internal multipleprediction algorithms. We build on this by considering sparsity promoting plane wave transformsto minimize artefacts and in general improve the prediction output. Furthermore, we argue forthe usage of demigration procedures to enable multidimensional internal multiple prediction withmigrated images, which also facilitate compliance with the strict data completeness requirementsof the ISS algorithm. We believe that a combination of these two techniques, sparsity promotingtransforms and demigration, pave the way for a wider application to new and legacy datasets.

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