2021
DOI: 10.1111/1365-2478.13086
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Pre‐migration diffraction separation using generative adversarial networks

Abstract: Diffraction imaging is the process of separating diffraction events from the seismic wavefield and imaging them independently, highlighting subsurface discontinuities. While there are many analytic‐based methods for diffraction imaging which use kinematic, dynamic or both, properties of the diffracted wavefield, they can be slow and require parameterization. Here, we propose an image‐to‐image generative adversarial network to automatically separate diffraction events on pre‐migrated seismic data in a fraction … Show more

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Cited by 11 publications
(3 citation statements)
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“…Considering the sparsity of small-scale geological bodies, Zhao et al (2021) used a hybrid L1-L2 norm to impose sparsity constraints on diffraction image to further focus diffraction and suppress remaining reflection. The singular value decomposition (Lin et al, 2020;Wang et al, 2020;Chen et al, 2023), Curvelet transform (Chen et al, 2022;Li et al, 2023), Mahalanobis distance (Zhao et al, 2016;Zhao et al, 2020;Li et al, 2019b), and deep learning method (Lowney et al, 2021;Sheng and Zhao, 2022;Kim et al, 2022;Sheng et al, 2023;) have also been applied to diffraction separation and imaging.…”
Section: Introductionmentioning
confidence: 99%
“…Considering the sparsity of small-scale geological bodies, Zhao et al (2021) used a hybrid L1-L2 norm to impose sparsity constraints on diffraction image to further focus diffraction and suppress remaining reflection. The singular value decomposition (Lin et al, 2020;Wang et al, 2020;Chen et al, 2023), Curvelet transform (Chen et al, 2022;Li et al, 2023), Mahalanobis distance (Zhao et al, 2016;Zhao et al, 2020;Li et al, 2019b), and deep learning method (Lowney et al, 2021;Sheng and Zhao, 2022;Kim et al, 2022;Sheng et al, 2023;) have also been applied to diffraction separation and imaging.…”
Section: Introductionmentioning
confidence: 99%
“…Lowney, Lokmer, O'Brien, Bean et al. (2021) trained the generative adversarial network (GAN) and showed that GAN separation is comparable to a benchmark separation created with PWD and performs up to 12 times faster. Lastly, Bauer et al.…”
Section: Introductionmentioning
confidence: 99%
“…Schwarz (2019) used wavefront filters for diffraction separation via the coherent summation and subtraction of seismic data. Lowney, Lokmer, O'Brien, Bean et al (2021) trained the generative adversarial network (GAN) and showed that GAN separation is comparable to a benchmark separation created with PWD and performs up to 12 times faster. Lastly, Bauer et al (2021) trained a deep convolutional neural network to decompose the wavefield of any input seismic or ground-penetrating-radar data into reflection, diffraction and noise and reported promising results.…”
Section: Introductionmentioning
confidence: 99%