Prestack Gaussian beam depth migration under complex surface conditions

Yue, Yubo; Li, Zhenchun; Zhang, Ping; Zhou, Xue; Qin, Ning

doi:10.1007/s11770-010-0238-0

Cited by 29 publications

(15 citation statements)

References 19 publications

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“…Bleistein and Gray (2010) proposed the method for computing the Hessian matrix in the formula of 3D prestack Gaussian beam migration by the steepest descent approximation. Yue et al (2012) extended Gray and Bleistein (2009) method to rugged topography by a phase correction method. Casasanta and Gray (2015) proposed converted-wave Gaussian beam migration, in which the steepest descent approximation was adopted to solve the migration formula.…”

Section: Introductionmentioning

confidence: 99%

A fast algorithm for prestack Gaussian beam migration adopting the steepest descent approximation

Gao

Sun

et al. 2017

Stud Geophys Geod

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In the past, prestack Gaussian beam migration adopted the steepest descent approximation to reduce the dimension of the integrals and speed up the computation. However, the simplified formula by the steepest descent approximation was still in the frequency domain, and it had to be evaluated at each frequency. To solve this problem, we present a fast algorithm by changing the order of the integrals. The innermost integral is regarded as a two-dimensional continuous function with respect to the real part and the imaginary part of the total traveltime. A lookup table corresponding to the value of the innermost integral is constructed at the sampling points. The value of the innermost integral at one imaging point can be obtained through interpolation in the constructed lookup table. The accuracy and efficiency of the fast algorithm are validated with the Marmousi dataset. The application to the Sigsbee2A dataset shows a good result.

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Section: Introductionmentioning

confidence: 99%

A fast algorithm for prestack Gaussian beam migration adopting the steepest descent approximation

Gao

Sun

et al. 2017

Stud Geophys Geod

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“…For practical applications, the computational efficiency is an important aspect. It is natural to couple the LSM concept and the ray-based wavefield extrapolator, the Gaussian beam propagator (e.g., Hill, 1990Hill, , 2001Hale, 1992aHale, , 1992bAlkhalifah, 1995;Gray, 2005;Gray and Bleistein, 2009;Popov et al, 2010;Yue et al, 2012;Zheng et al, 2013) to obtain faster turn-around time, while retaining the benefit of LSM.…”

Section: Introductionmentioning

confidence: 99%

Least-squares Gaussian beam migration

Liu

Zheng

et al. 2016

GEOPHYSICS

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Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

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“…; Yue et al . ; Han, Wang, and Lu ; Han et al . ), as an alternative method to Kirchhoff migration, is an elegant and effective depth migration method, with imaging accuracy comparable with that of wave‐equation migration.…”

Section: Introductionmentioning

confidence: 99%

“…Yue et al . () presented a relatively amplitude‐preserved Gaussian beam migration method for complex surface conditions, which directly decompose the seismic records into a local plane‐wave component from the rugged topography by the local slant stack.…”

Section: Introductionmentioning

confidence: 99%

Multiwave Gaussian beam prestack depth migration of exploration‐scale seismic data with complex near‐surface effects

Han

Wang

2016

Near Surface Geophysics

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The direct prestack depth migration methods of nonplanar data provide effective ways to address the complex near‐surface problem. Based on the study of local slant stacks considering the elevation and dip angle information of an irregular surface, an alternative method for multiwave Gaussian beam prestack depth migration of exploration‐scale seismic data with complex near‐surface effects is presented here. We first derive the downward‐continued wavefield for an irregular surface in terms of Gaussian beams, where we directly decompose the seismic records within a Gaussian window into a local plane‐wave component. The PP‐ and PS‐wave Gaussian beam prestack depth migration methods under complex near‐surface conditions are then presented, after the wavefield separation is performed with an affine coordinate‐system transform. Tests with numerical examples demonstrate the accuracy and effectiveness of the method.

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Prestack Gaussian beam depth migration under complex surface conditions

Cited by 29 publications

References 19 publications

A fast algorithm for prestack Gaussian beam migration adopting the steepest descent approximation

A fast algorithm for prestack Gaussian beam migration adopting the steepest descent approximation

Least-squares Gaussian beam migration

Multiwave Gaussian beam prestack depth migration of exploration‐scale seismic data with complex near‐surface effects

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