Comparison of the inverse scattering series free-surface multiple elimination (ISS FSME) algorithm with the industry-standard surface-related multiple elimination (SRME): Defining the circumstances in which each method is the appropriate toolbox choice

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S459-S478
Author(s):  
Chao Ma ◽  
Qiang Fu ◽  
Arthur B. Weglein
Keyword(s):  
Free Surface ◽  
Inverse Scattering ◽  
Energy Minimization ◽  
Cost Effective ◽  
Adaptive Process ◽  
Industry Standard ◽  
Inverse Scattering Series ◽  
The Difference ◽  
Multiple Prediction ◽  
Multiple Elimination

The industry-standard surface-related multiple elimination (SRME) method provides an approximate predictor of the amplitude and phase of free-surface multiples. This approximate predictor then calls upon an energy-minimization adaptive subtraction step to bridge the difference between the SRME prediction and the actual free-surface multiple. For free-surface multiples that are proximal to other events, the criteria behind energy-minimization adaptive subtraction can be invalid. When applied under these circumstances, a proximal primary can often be damaged. To reduce the dependence on the adaptive process, a more accurate free-surface multiple prediction is required. The inverse scattering series (ISS) free-surface multiple elimination (FSME) method predicts free-surface multiples with accurate time and accurate amplitude of free-surface multiples for a multidimensional earth, directly and without any subsurface information. To quantify these differences, a comparison with analytic data was carried out, confirming that when a free-surface multiple interferes with a primary, applying SRME with adaptive subtraction can and will damage the primary, whereas ISS free-surface elimination will precisely remove the free-surface multiple without damaging the interfering primary. On the other hand, if the free-surface multiple is isolated, then SRME with adaptive subtraction can be a cost-effective toolbox choice. SRME and ISS FSME each have an important and distinct role to play in the seismic toolbox, and each method is the indicated choice under different circumstances.

Removing free-surface multiples from teleseismic transmission and constructed reflection responses using reciprocity and the inverse scattering series

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. SI71-SI78 ◽  
Author(s):  
Chengliang Fan ◽  
Gary L. Pavlis ◽  
Arthur B. Weglein ◽  
Bogdan G. Nita
Keyword(s):  
Free Surface ◽  
Inverse Scattering ◽  
Synthetic Data ◽  
P Wave ◽  
Transmission Data ◽  
Before And After ◽  
Small Incidence ◽  
Inverse Scattering Series ◽  
Acoustic Media ◽  
The Difference

We develop a new way to remove free-surface multiples from teleseismic P- transmission and constructed reflection responses. We consider two types of teleseismic waves with the presence of the free surface: One is the recorded waves under the real transmission geometry; the other is the constructed waves under a virtual reflection geometry. The theory presented is limited to 1D plane wave acoustic media, but this approximation is reasonable for the teleseismic P-wave problem resulting from the steep emergence angle of the wavefield. Using one-way wavefield reciprocity, we show how the teleseismic reflection responses can be reconstructed from the teleseismic transmission responses. We use the inverse scattering series to remove free-surface multiples from the original transmission data and from the reconstructed reflection response. We derive an alternative algorithm for reconstructing the reflection response from the transmission data that is obtained by taking the difference between the teleseismic transmission waves before and after free-surface multiple removal. Numerical tests with 1D acoustic layered earth models demonstrate the validity of the theory we develop. Noise test shows that the algorithm can work with S/N ratio as low as 5 compared to actual data with S/N ratio from 30 to 50. Testing with elastic synthetic data indicates that the acoustic algorithm is still effective for small incidence angles of typical teleseismic wavefields.

scholarly journals Relating source-receiver interferometry to an inverse-scattering series to derive a new method to estimate internal multiples

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. Q27-Q40 ◽  
Author(s):  
Katrin Löer ◽  
Andrew Curtis ◽  
Giovanni Angelo Meles
Keyword(s):  
Inverse Scattering ◽  
Computational Cost ◽  
Synthetic Data ◽  
Data Set ◽  
Inverse Scattering Series ◽  
Internal Multiples ◽  
Multiple Prediction ◽  
New Formula ◽  
3D Applications ◽  
Insight Into

We have evaluated an explicit relationship between the representations of internal multiples by source-receiver interferometry and an inverse-scattering series. This provides a new insight into the interaction of different terms in each of these internal multiple prediction equations and explains why amplitudes of estimated multiples are typically incorrect. A downside of the existing representations is that their computational cost is extremely high, which can be a precluding factor especially in 3D applications. Using our insight from source-receiver interferometry, we have developed an alternative, computationally more efficient way to predict internal multiples. The new formula is based on crosscorrelation and convolution: two operations that are computationally cheap and routinely used in interferometric methods. We have compared the results of the standard and the alternative formulas qualitatively in terms of the constructed wavefields and quantitatively in terms of the computational cost using examples from a synthetic data set.

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.

scholarly journals Data-driven internal multiple elimination and its consequences for imaging: A comparison of strategies

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S365-S372 ◽  
Author(s):  
Lele Zhang ◽  
Jan Thorbecke ◽  
Kees Wapenaar ◽  
Evert Slob
Keyword(s):  
Inverse Scattering ◽  
Computational Cost ◽  
Multiple Reflection ◽  
Data Driven ◽  
Transmission Losses ◽  
Multiple Reflections ◽  
Data Set ◽  
Numerical Examples ◽  
Inverse Scattering Series ◽  
Multiple Elimination

We have compared three data-driven internal multiple reflection elimination schemes derived from the Marchenko equations and inverse scattering series (ISS). The two schemes derived from Marchenko equations are similar but use different truncation operators. The first scheme creates a new data set without internal multiple reflections. The second scheme does the same and compensates for transmission losses in the primary reflections. The scheme derived from ISS is equal to the result after the first iteration of the first Marchenko-based scheme. It can attenuate internal multiple reflections with residuals. We evaluate the success of these schemes with 2D numerical examples. It is shown that Marchenko-based data-driven schemes are relatively more robust for internal multiple reflection elimination at a higher computational cost.

Enhancing Internal Multiple Prediction by Using the Inverse Scattering Series

2020 ◽  
Author(s):  
J. Wu ◽  
Z. James Wu ◽  
F. Xavier de Melo ◽  
C. Lapilli ◽  
C. Kostov
Keyword(s):  
Inverse Scattering ◽  
Inverse Scattering Series ◽  
Multiple Prediction

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.

Internal multiple prediction using inverse scattering series withsparsity promotionPart II: Application strategy and field data examples

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Ole Edvard Aaker ◽  
Adriana Citlali Ramírez ◽  
Emin Sadikhov
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Norwegian Sea ◽  
Prediction Quality ◽  
Inverse Scattering Series ◽  
Exploration And Production ◽  
Internal Multiples ◽  
Multiple Prediction ◽  
Seismic Images

Incorrect imaging of internal multiples can lead to substantial imaging artefacts. It is estimatedthat the majority of seismic images available to exploration and production companies have had nodirect attempt at internal multiple removal. In Part I of this article we considered the role of spar-sity promoting transforms for improving practical prediction quality for algorithms derived fromthe inverse scattering series (ISS). Furthermore, we proposed a demigration-migration approach toperform multidimensional internal multiple prediction with migrated data and provided a syntheticproof of concept. In this paper (Part II) we consider application of the demigration-migration approach to field data from the Norwegian Sea, and provide a comparison to a post-stack method (froma previous related work). Beyond application to a wider range of data with the proposed approach,we consider algorithmic and implementational optimizations of the ISS prediction algorithms tofurther improve the applicability of the multidimensional formulations.

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