Enhancing internal multiple prediction by using the inverse scattering series: methodology and field application

Geophysics ◽  
2021 ◽  
pp. 1-70
Author(s):  
Jing Wu ◽  
Zhiming Wu ◽  
Frederico Xavier de Melo ◽  
Cintia Mariela Lapilli ◽  
Clément Kostov ◽  
...  
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Field Application ◽  
Nearest Neighbor Search ◽  
Opening Angle ◽  
Model Quality ◽  
Inverse Scattering Series ◽  
Multiple Prediction

We introduce four approaches that dramatically enhance the application of the inverse scattering series method for field data internal multiple prediction. The first approach aims to tackle challenges related to input data conditioning and interpolation. We addressed this through an efficient and fit-for-purpose data regularization strategy, which in this work was a nearest-neighbor search followed by differential moveout to accommodate various acquisition configurations. The second approach addresses cost challenges through applying angle constraints over both the dip angle and opening angle, reducing computational cost without compromising the model’s quality. We also propose an automatic solution for parameterization. The third approach segments the prediction by limiting the range of the multiple’s generator, which can benefit the subsequent adaptive subtraction. The fourth approach works on improving predicted model quality. The strategy includes correctly incorporating the 3D source effect and obliquity factor to enhance the amplitude fidelity of the predicted multiples in terms of frequency spectrum and angle information. We illustrate challenges and report on the improvements in cost, quality or both from the new innovative approaches, using examples from synthetic data and from three field data 2D lines representative of shallow and of deep water environments.

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.

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.

Enhancing internal multiple prediction for field data application by using the inverse scattering series

2020 ◽  
Author(s):  
Jing Wu ◽  
Frederico Xavier de Melo ◽  
Cintia Mariela Lapilli ◽  
Clement Kostov ◽  
Zhiming James Wu
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Data Application ◽  
Inverse Scattering Series ◽  
Multiple Prediction

scholarly journals Semi-supervised hash learning method with consistency-based dimensionality reduction

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881917
Author(s):  
Fang Lv ◽  
Yuliang Wei ◽  
Xixian Han ◽  
Bailing Wang
Keyword(s):  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Critical Role ◽  
Computational Cost ◽  
High Volume ◽  
Nearest Neighbor Search ◽  
Semantic Features ◽  
Learning Method ◽  
Retrieval Performance ◽  
Low Dimensional

With the explosive growth of surveillance data, exact match queries become much more difficult for its high dimension and high volume. Owing to its good balance between the retrieval performance and the computational cost, hash learning technique is widely used in solving approximate nearest neighbor search problems. Dimensionality reduction plays a critical role in hash learning, as its target is to preserve the most original information into low-dimensional vectors. However, the existing dimensionality reduction methods neglect to unify diverse resources in original space when learning a downsized subspace. In this article, we propose a numeric and semantic consistency semi-supervised hash learning method, which unifies the numeric features and supervised semantic features into a low-dimensional subspace before hash encoding, and improves a multiple table hash method with complementary numeric local distribution structure. A consistency-based learning method, which confers the meaning of semantic to numeric features in dimensionality reduction, is presented. The experiments are conducted on two public datasets, that is, a web image NUS-WIDE and text dataset DBLP. Experimental results demonstrate that the semi-supervised hash learning method, with the consistency-based information subspace, is more effective in preserving useful information for hash encoding than state-of-the-art methods and achieves high-quality retrieval performance in multi-table context.

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.

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