Fast and robust common-reflection-surface parameter estimation

Waldeland, Anders U.; Zhao, Hua-An; Faccipieri, Jorge H.; Solberg, Anne; Gelius, Leiv‐J.

doi:10.1190/geo2017-0113.1

Cited by 14 publications

(13 citation statements)

References 27 publications

(41 reference statements)

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“…The method proposed by Waldeland et al . () gave a more robust estimation of the CRS parameters than the method proposed in Santos et al . ().…”

Section: Introductionmentioning

confidence: 95%

“…In the approach proposed in Waldeland et al . () for fast estimation of zero offset common reflection surface (ZO CRS) parameters, the gradient structure tensor (GST) and the quadratic gradient structure tensor (QST) were applied to stacked zero offset (ZO) sections. The results presented in Waldeland et al .…”

Section: The Structure Tensor Approachmentioning

confidence: 99%

“…This method was further developed in Waldeland et al . () where the parameters are estimated from the stacked ZO section. The derivatives with respect to midpoint were estimated using the gradient structure tensor (GST) (Bigun and Granlund ) and quadratic gradient structure tensor (QST) (Bakker ).…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we extend the method in Waldeland et al . () from ZO CRS to FO CRS. This is referred to as the structure tensor approach.…”

Section: Introductionmentioning

confidence: 99%

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Fast estimation of prestack common reflection surface parameters

Waldeland

Coimbra

Faccipieri

et al. 2019

Geophysical Prospecting

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A B S T R A C TWe present a method for fast estimation of finite offset common reflection surface parameters. Firstly, the derivatives with respect to offset are derived from the velocity guide. Secondly, we apply structure tensors to extract the derivatives with respect to midpoint from stacked common offset sections. Finally, the mixed derivative is estimated using a one-parametric semblance search. The proposed method is compared to the global five-parametric semblance search and the pragmatic sequential two-parametric semblance search on one synthetic and one real data set. The experiments show that the proposed method is more robust against noise than the pragmatic search and have comparable robustness with the global search. The proposed method smoothes parameter estimates in a local window, and the window size is set to give the best trade-off between detail and robustness. Since the proposed method is dependent on a velocity guide, the quality of the other parameter estimates may be influenced by any inaccuracies in the guide. The main advantage of the proposed method is the computational efficiency. When compared with a gridded implementation of the semblance search, the proposed method is 10 and 400 times faster than the pragmatic and global search. Alternative search strategies significantly reduce the computational cost of the global search. However, since more than 99% of the computational cost of the proposed method comes from the semblance search to estimate the mixed derivative, it is expected that such techniques also reduce the computational cost for the proposed method.

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“…The method proposed by Waldeland et al . () gave a more robust estimation of the CRS parameters than the method proposed in Santos et al . ().…”

Section: Introductionmentioning

confidence: 95%

Section: The Structure Tensor Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this work, we extend the method in Waldeland et al . () from ZO CRS to FO CRS. This is referred to as the structure tensor approach.…”

Section: Introductionmentioning

confidence: 99%

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Fast estimation of prestack common reflection surface parameters

Waldeland

Coimbra

Faccipieri

et al. 2019

Geophysical Prospecting

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“…Note that our model used in this example is very similar to that in the work of Waldeland et al . (). Acoustic finite difference modelling method is used with both sources and receivers at the surface (Sun et al .…”

Section: Application On a 2d Common Reflection Surface Stacking Problemmentioning

confidence: 97%

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Acuna

Sun²

2020

Geophysical Prospecting

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A B S T R A C TAlthough Genetic Algorithms have found many successful applications in the field of exploration geophysics, the convergence speed remains a big challenge as Genetic Algorithms usually require a huge amount of fitness function evaluations. In this paper, we propose an efficiency-improved Genetic Algorithm, which has both a good global search capability and a good local search capability, and is also capable of robustly handling the premature convergence challenge commonly seen in linear and directed non-linear optimization methods. In our new genetic algorithm, the global search capability is performed via a modified island model, while the local search capability is provided by a novel self-adaptive differential evolution fine tuning scheme. Premature convergence is dealt with via a local exhaustive search method. We first demonstrate the much improved convergence speed of this efficiency-improved Genetic Algorithm over that of our previously proposed advanced Genetic Algorithm on several multimodal functions. We further demonstrate the effectiveness of our efficiency-improved Genetic Algorithm by applying it to a two-dimensional common reflection surface stacking problem, which is a highly nonlinear geophysical optimization problem, to obtain very encouraging results.

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Low-rank diffraction separation using an improved MSSA algorithm

Lin

Zhao

2021

Acta Geophys.

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Fast and robust common-reflection-surface parameter estimation

Cited by 14 publications

References 27 publications

Fast estimation of prestack common reflection surface parameters

Fast estimation of prestack common reflection surface parameters

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Low-rank diffraction separation using an improved MSSA algorithm

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