An illumination‐compensated Gaussian beam migration for enhancing subsalt imaging

Liu, Shaoyong; Yan, Zhe; Zhu, Wu; Han, Bingkai; Gu, Hanming; Hu, Shanzheng

doi:10.1111/1365-2478.13117

Cited by 16 publications

(5 citation statements)

References 42 publications

(58 reference statements)

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“…Today, the wide-azimuth and high-fidelity seismic datasets enable the high dimensional inversion imaging to estimate the subsurface parameters with higher resolution. The classic FWI is a popular high dimensional nonlinear inversion method from seismic data to subsurface model but usually suffers the "cycle-skipping" problem [55]. In this study, we focused on the broadband reflectivity under the linear LSM inversion frame and then estimated the broadband AI model by incorporating the background AI model.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, the computational cost increased nearly 1600% when FD-based methods are used to propagate seismic wave with frequency from 30 Hz to 70 Hz, while beam-based method only requires less than a 90% increment in the same case [19]. Moreover, the GBM is a one-way propagator that does not have a back-scattering wave in propagation, and the GBM-based method has no problem of crosstalk noises in PSF calculation due to interferometry of scatter-points [46,55].…”

Section: Discussionmentioning

confidence: 99%

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An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

et al. 2021

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The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

et al. 2021

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“…In order to enhance the resolution of seismic images using PSF, it is crucial to generate an appropriate PSF distribution of the subsurface model. Various methods have been proposed for generating PSFs, including ray‐based (S. Liu et al., 2021; Xu et al., 2022; Yang et al., 2022), wave‐equation based (Jensen et al., 2021; Toxopeus et al., 2008) and demigration–migration methods (Aoki & Schuster, 2009; Fletcher et al., 2012; C. Liu & Fu, 2021). Ray‐based methods are computationally efficient but suffer from low accuracy, while wave‐equation based methods are computationally expensive to simulate the wave propagation process.…”

Section: Theory and Methodsmentioning

confidence: 99%

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Liu,

Sun,

et al. 2024

Geophysical Prospecting

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Improving the resolution of seismic migration images plays an important role for geophysical interpreters to characterize underground reservoirs. However, the classical image domain least‐squares migration method based on the local‐stationary assumption cannot obtain a satisfactory high‐resolution seismic image due to the significant spatial variant characteristics of the point spread function. To mitigate this problem, we proposed a high‐resolution point spread function deconvolution method and applied it to two‐dimensional cases. Nevertheless, extending the two‐dimensional method to three‐dimensional problems directly would fail due to the intrinsic complexity in three‐dimensional cases. In this study, we resolve the differences encountered in the point spread function deconvolution method for two‐ and three‐dimensional cases and provide specific strategies for achieving high‐resolution imaging with low computational cost when extending the point spread function deconvolution method to three‐dimensional cases. The main schemes include (1) incorporating the analytical expression of the point spread function to guide the generation of three‐dimensional point spread function distributions, (2) extending the point spread function filter calculation method from a two‐dimensional square to a three‐dimensional rectangular prism and (3) interpolating to obtain more compact point spread functions for reducing migration artefacts. Results from the three‐dimensional synthetic Overthrust model and field data set demonstrate that our techniques could effectively enhance the spatial resolution of the migration images with reduced migration artefacts. With these specific strategies, the space‐variant point spread function deconvolution algorithm shows superior performance on three‐dimensional cases at a much lower computational cost compared with the classical least‐squares migration method and the local‐stationary deblurring method. Synthetic tests and real data applications confirm that the space‐variant point spread function deconvolution method has distinct advantages over both two‐ and three‐dimensional problems and can be widely adopted in practice.

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“…PSF represents the "point spread function" [31] and here is set as the spatial weights of NDFT. The PSF is defined as the following formula:…”

Section: Overview On Fourier Reconstruction Sparse Inversion 21 Non-u...mentioning

confidence: 99%

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

2022

Teh. vjesn.

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A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments.

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An illumination‐compensated Gaussian beam migration for enhancing subsalt imaging

Cited by 16 publications

References 42 publications

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

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