Residual statics correction without NMO – A rank-based approach

Alfaraj, Ali M.; Verschuur, Eric; Herrmann, Felix J.

doi:10.1190/segam2021-3583455.1

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Even though surface-consistent approaches have been initially developed for reflection seismology, they also can be applied to the transmitted part of the seismic wavefield, which is the one mostly traveling in the near surface and mostly sensing its complex heterogeneities (Colombo et al, 2021). In addition, surface-consistent statics correction methods can address most of the statics and therefore are routinely applied in seismic data processing; it is worth mentioning that nonsurface-consistent statics correction approaches also are possible, sometimes providing more accurate near-surface corrections (Alfaraj et al, 2021).…”

Section: Spm For Rrsementioning

confidence: 99%

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

et al. 2023

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Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms and a use-case, however, proves to be a very challenging task especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We show that maximizing stack-power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space, and define a proper encoding to translate the problem-variables into qubit states. We show that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Lastly, we show that a generic, black-box, hybrid classical-quantum solver could also be used to solve stack-power maximization problems proximal to industrial relevance, and capable to surpassing deterministic solvers prone to cycle-skipping. A custom-build workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.

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Section: Spm For Rrsementioning

confidence: 99%

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

et al. 2023

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“…To avoid the above limitations by estimation and correction of surface-and non-surface-consistent statics without NMO correction, Alfaraj et al (2021Alfaraj et al ( , 2022 propose a low-rank-based residual statics (LR-ReS) estimation and correction framework. Since LR-ReS estimation and correction is not limited by the surface-consistency assumption and, it is capable of estimating more accurate statics.…”

Section: Introductionmentioning

confidence: 99%

Towards 3D near-surface correction without NMO – A rank-based approach

Alfaraj

Verschuur

2023

84th EAGE Annual Conference &Amp; Exhibition

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To avoid multiple iterations of normal moveout (NMO) velocity estimation followed by shortwavelength statics estimation usually performed on land data, and to also improve the accuracy and computational efficiency of the latter, a low-rank-based residuals statics (LR-ReS) estimation and correction framework has been recently proposed. The method iteratively promotes the low-rank structure in the midpoint-offset-frequency domain of 2D data as statics-free data can be approximated by low-rank matrices, while data influenced by the weathering layers exhibits slow singular values decay. For 3D data, there exist different options to organize it into 2D matrices to be able to compute the singular value decomposition (SVD) required for low-rank approximation. It is also essential to find an organization that reveals the rank structure. We examine the different organization options. Based on finding a suitable sorting domain, we extend the LR-ReS estimation and correction to 3D data. We demonstrate the performance of the method on simulated data and will show field data results during the presentation.

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A Weighted Closure-Phase Statics Correction Method: Synthetic and Field Data Examples

Hanafy

Liu

2022

IEEE Trans. Geosci. Remote Sensing

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Residual statics correction without NMO – A rank-based approach

Cited by 3 publications

References 13 publications

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

Towards 3D near-surface correction without NMO – A rank-based approach

A Weighted Closure-Phase Statics Correction Method: Synthetic and Field Data Examples

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