The inverse scattering series depth imaging algorithms: development, tests and progress towards field data application

2010 ◽  
Author(s):  
Arthur B. Weglein ◽  
Fang Liu ◽  
Zhiqiang Wang ◽  
Xu Li ◽  
Hong Liang
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Depth Imaging ◽  
Data Application ◽  
Inverse Scattering Series ◽  
Imaging Algorithms

First field data examples of inverse scattering series direct depth imaging without the velocity model

2012 ◽  
Author(s):  
Arthur B. Weglein ◽  
Fang Liu ◽  
Xu Li ◽  
Paolo Terenghi ◽  
Ed Kragh ◽  
...  
Keyword(s):  
Inverse Scattering ◽  
Field Data ◽  
Velocity Model ◽  
Depth Imaging ◽  
Inverse Scattering Series

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

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 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.

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