Extracting Fresnel zones from migrated dip-angle gathers using a convolutional neural network

References

1. Chen, J.2004. Specular ray parameter extraction and stationary-phase migration. Geophysics69: 249–56.
   [Google Scholar]
2. Hinton, G.E., S.Osindero, and Y.W.Teh. 2006. A fast learning algorithm for deep belief nets. Neural Computation18, no. 7: 1527–54.
   [Google Scholar]
3. Klokov, A., and S.Fomel. 2012a. Optimal migration aperture for conflicting dips. 82nd Annual International Meeting, SEG, Expanded Abstracts.
4. Klokov, A., and S.Fomel. 2012b. Separation and imaging of seismic diffractions using migrated dip-angle gathers. Geophysics77, no. 6: S131–S143.
   [Google Scholar]
5. Krizhevsky, A., I.Sutskever, and G.E.Hinton. 2012. ImageNet classification with deep convolutional neural networks. In NIPS, 1106–14.
6. Kumar, P.C., and A.Mandal. 2018. Enhancement of fault interpretation using multi-attribute analysis and artificial neural network (ANN) approach: A case study from Taranaki Basin, New Zealand. Exploration Geophysics49, no. 3: 409–24.
   [Google Scholar]
7. LeCun, Y., Y.Bengio, and G.Hinton. 2015. Deep learning. Nature521: 436–44.
   [Google Scholar]
8. LeCun, Y., L.Bottou, G.Orr, and K.R.Muller. 1998. Efficient backprop. In Neural networks: Tricks of the trade, ed. Grégoire Montavon, Geneviève Orr, and Klaus-Robert Müller, 9–48. New York: Springer.
9. Lewis, W., and D.Vigh. 2017. Deep learning prior models from seismic images for full-waveform inversion. SEG International Exposition and Annual Meeting, Society of Exploration Geophysicists, 1512–17.
10. Marfurt, K.J.2006. Robust estimates of 3D reflector dip and azimuth. Geophysics71, no. 4: P29–P40.
    [Google Scholar]
11. Plotnitskii, P., T.Alkhalifah, O.Ovcharenko, and V.Kazei. 2019. Seismic model low wavenumber extrapolation by a deep convolutional neural network. ASEG Extended Abstracts1: 1–5.
    [Google Scholar]
12. Robbins, H., and S.Monro. 1951. A stochastic approximation method. The Annals Of Mathematical Statistics22: 400–7.
    [Google Scholar]
13. Rumelhart, D.E., G.E.Hinton, and R.J.Williams. 1986c. Learning representations by back-propagating errors. Nature323: 533–6.
    [Google Scholar]
14. Schleicher, J., P.Hubral, M.Tygel, and M.S.Jaya. 1997. Minimum apertures and Fresnel zones in migration and demigration. Geophysics62: 183–94.
    [Google Scholar]
15. Schmidhuber, J.2015. Deep learning. Scholarpedia10, no. 11: 1527–54. CiteSeerX 10.1.1.76.1541.
    [Google Scholar]
16. Simonyan, K., and A.Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
17. Wang, B., N.Zhang, W.Lu, and J.Wang. 2019. Deep-learning-based seismic data interpolation: A preliminary result. Geophysics84, no. 1: V11–V20. CiteSeerX 10.1.1.76.1541.
    [Google Scholar]
18. Wu, X., L.Liang, Y.Shi, and S.Fomel. 2019. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics84, no. 3: IM35–IM45. CiteSeerX 10.1.1.76.1541.
    [Google Scholar]
19. Xu, J., and J.Zhang. 2017. Prestack time migration of nonplanar data: improving topography prestack time migration with dip-angle domain stationary-phase filtering and effective velocity inversion. Geophysics82, no. 3: S235–S246.
    [Google Scholar]
20. Yu, Z., J.Etgen, D.Whitcombe, L.Hodgson, and H.Liu. 2013. Dip-adaptive operator anti-aliasing for Kirchhoff migration. 83rd Annual International Meeting, SEG, Expanded Abstracts, 3692–95.
21. Zeiler, M.D., and R.Fergus. 2013. Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557.
22. Zhang, J., Z.Li, L.Liu, J.Wang, and J.Xu. 2016. High-resolution imaging: An approach by incorporating stationary-phase implementation into deabsorption prestack time migration. Geophysics81, no. 5: S317–S331.
    [Google Scholar]
23. Li, Z., J.Zhang, and W.Liu. 2018. Diffraction imaging using dip-angle and offset gathers. Chinese Journal of Geophysics61, no. 4: 1447–59.
    [Google Scholar]