References
- Abma, R., and Kabir, N., 2006, 3D interpolation of irregular data with a POCS algorithm, Geophysics, 71(6), E91-E97., doi:10.1190/1.2356088
- Cai, J. F., Ji, H., Shen, Z., and Ye, G. B, 2014, Data-driven tight frame construction and image denoising, Appl. Comput. Harmon. Anal., 37(1), 89-105., doi: 10.1016/j.acha.2013.10.001
- Chai, X., Gu, H., Li, F., Duan, H., Hu, X., and Lin, K., 2020, Deep learning for irregularly and regularly missing data reconstruction, Scientific Reports, 10(1), 1-18., doi: 10.1016/j.acha.2013.10.001
- Chang, D. K., Yang, W. Y., Yong, X. S., and Li, H. S., 2019, Seismic data interpolation with conditional generative adversarial network in time and frequency domain, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2589-2593., doi: 10.1190/segam2019-3210118.1
- Chang, D., Yang, W., Yong, X., and Li, H., ,2018, Generative adversarial networks for seismic data interpolation, In SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, Global Meeting Abstracts, 40-43, doi: 10.1190/AIML2018-11.1
- Chen, Y., Zhang, D., Jin, Z., Chen, X., Zu, S., Huang, W., and Gan, S., 2016, Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method, Geophys. J. Int. 206(3), 1695-1717., doi: 10.1093/gji/ggw230
- Cunha, A., Pochet, A., Lopes, H., and Gattass, M., 2020, Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data, Comput. Geosci., 135, 104344., doi: 10.1016/j.cageo.2019.104344
- Di, H., 2018, Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation, arXiv preprint arXiv:1810.08517
- Fomel, S., 2002, Applications of plane-wave destruction filters, Geophysics, 67(6), 1946-1960., doi: 10.1190/1.1527095
- Fomel, S., 2003, Seismic reflection data interpolation with differential offset and shot continuation, Geophysics, 68(2), 733-744., doi: 10.1190/1.1567243
- Gan, S., Wang, S., Chen, Y., Zhang, Y., and Jin, Z., 2015, Dealiased seismic data interpolation using seislet transform with low-frequency constraint. IEEE Geosci. Remote Sens. Lett., 12(10), 2150-2154., doi: 10.1109/LGRS.2015.2453119
- Gao, J., Sacchi, M. D., and Chen, X., 2013a, A fast reducedrank interpolation method for prestack seismic volumes that depend on four spatial dimensions, Geophysics, 78(1), V21-V30., doi: 10.1190/geo2012-0038.1
- Gao, J., Stanton, A., and Sacchi, M. D., 2015, Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising, Geophysics, 80(6), V173-V187., doi: 10.1190/geo2014-0594.1
- Gao, J., Stanton, A., Naghizadeh, M., Sacchi, M. D., & Chen, X., 2013b, Convergence improvement and noise attenuation considerations for beyond alias projection onto convex sets reconstruction, Geophys. Prospect., 61, 138-151., doi: 10.1111/j.1365-2478.2012.01103.x
- Geron, A., 2017, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, Sebastopol, CA, 54-56.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., 2014, Generative adversarial nets, In Advances in neural information processing systems (pp. 2672-2680).
- He, K., Zhang, X., Ren, S., and Sun, J., 2016, Deep residual learning for image recognition, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 770-778., doi: 10.1109/CVPR.2016.90
- Herrmann, F. J., and Hennenfent, G., 2008, Non-parametric seismic data recovery with curvelet frames, Geophys. J. Int., 173(1), 233-248., doi: 10.1111/j.1365-246X.2007.03698.x
- Isola, P., Zhu, J. Y., Zhou, T., and Efros, A. A., 2017, Image-toimage translation with conditional adversarial networks, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, 1125-1134., doi: 10.1109/CVPR.2017.632
- Jia, Y., and Ma, J., 2017, What can machine learning do for seismic data processing? An interpolation application, Geophysics, 82(3), V163-V177., doi: 10.1190/geo2016-0300.1
- Jia, Y., Yu, S., and Ma, J., 2018, Intelligent interpolation by Monte Carlo machine learning, Geophysics, 83(2), V83-V97., doi: 10.1190/geo2017-0294.1
- Kaur, H., Pham, N., and Fomel, S., 2019, Seismic data interpolation using CycleGAN, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2202-2206., doi:10.1190/segam2019-3207424.1
- Keys, R., 1981, Cubic convolution interpolation for digital image processing, IEEE Trans. Acoust., 29(6), 1153-1160., doi: 10.1109/TASSP.1981.1163711
- Li, S., Yang, C., Sun, H., and Zhang, H., 2019, Seismic fault detection using an encoder-decoder convolutional neural network with a small training set, Journal of Geophysics and Engineering, 16(1), 175-189., doi: 10.1093/jge/gxy015
- Liang, J., Ma, J., and Zhang, X., 2014, Seismic data restoration via data-driven tight frame, Geophysics, 79(3), V65-V74., doi: 10.1190/geo2013-0252.1
- Liu, W., Cao, S., Gan, S., Chen, Y., Zu, S., and Jin, Z., 2016, One-step slope estimation for dealiased seismic data reconstruction via iterative seislet thresholding, IEEE Geosci. Remote Sens. Lett., 13(10), 1462-1466., doi:10.1109/LGRS.2016.2591939
- Liu, Y., and Fomel, S., 2011, Seismic data interpolation beyond aliasing using regularized nonstationary autoregression, Geophysics, 76(5), V69-V77., doi: 10.1190/geo2010-0231.1
- Ma, J., 2013, Three-dimensional irregular seismic data reconstruction via low-rank matrix completion, Geophysics, 78(5), V181-V192., doi: 10.1190/geo2012-0465.1
- Maaten, L. V. D., and Hinton, G., 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 2579-2605.
- Mandelli, S., Borra, F., Lipari, V., Bestagini, P., Sarti, A., and Tubaro, S., 2018, Seismic data interpolation through convolutional autoencoder, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4101-4105., doi:10.1190/segam2018-2995428.1
- Mirza, M., and Osindero, S., 2014, Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784.
- Naghizadeh, M., and Innanen, K. A.., 2011, Seismic data interpolation using a fast generalized Fourier transform, Geophysics, 76(1), V1-V10., doi: 10.1190/1.3511525
- Naghizadeh, M., and Sacchi, M. D., 2007, Multistep autoregressive reconstruction of seismic records, Geophysics, 72(6), V111-V118., doi: 10.1190/1.2771685
- Naghizadeh, M., and Sacchi, M. D., 2010, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data, Geophysics, 75(6), WB189-WB202., doi: 10.1190/1.3509468
- Oliveira, D. A., Ferreira, R. S., Silva, R., and Brazil, E. V., 2018, Interpolating seismic data with conditional generative adversarial networks, IEEE Geosci. Remote Sens. Lett., 15(12), 1952-1956., doi: 10.1109/LGRS.2018.2866199
- Oropeza, V., and Sacchi, M., 2011, Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis, Geophysics, 76(3), V25-V32., doi: 10.1190/1.3552706
- Park, J., Yoon, D., Seol, S. J., and Byun, J., 2019, Reconstruction of seismic field data with convolutional U-Net considering the optimal training input data, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4650-4654., doi:10.1190/segam2019-3216017.1
- Ronen, J., 1987, Wave-equation trace interpolation, Geophysics, 52(7), 973-984., doi: 10.1190/1.1442366
- Ronneberger, O., Fischer, P., and Brox, T., 2015, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
- Spitz, S., 1991, Seismic trace interpolation in the FX domain, Geophysics, 56(6), 785-794. doi: 10.1190/1.1443096
- Trickett, S., Burroughs, L., Milton, A., Walton, L., and Dack, R., 2010, Rank-reduction-based trace interpolation, 80th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 3829-3833., doi: 10.1190/1.3513645
- Wang, B., Zhang, N., and Lu, W., 2018, Intelligent shot gather reconstruction using residual learning networks, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2001-2005., doi: 10.1190/segam2018-2997541.1
- Wang, B., Zhang, N., Lu, W., and Wang, J., 2019, Deeplearning-based seismic data interpolation: A preliminary result, Geophysics, 84(1), V11-V20., doi: 10.1190/geo2017-0495.1
- Wang, Y., Wang, B., Tu, N., and Geng, J., 2020, Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder, Geophysics, 85(2), V119-V130., doi: 10.1190/geo2018-0699.1
- Wu, X., Liang, L., Shi, Y., and Fomel, S., 2019, FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation, Geophysics, 84(3), IM35-IM45., doi: 10.1190/geo2018-0646.1
- Yoon, D., Yeeh, Z., and Byun, J., 2020, Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections, IEEE Geosci. Remote Sens. Lett.
- Yu, S., Ma, J., and Osher, S., 2016, Monte Carlo data-driven tight frame for seismic data recovery, Geophysics, 81(4), V327-V340., doi: 10.1190/geo2015-0343.1
- Yu, S., Ma, J., Zhang, X., and Sacchi, M., 2015, Denoising and interpolation of high-dimensional seismic data by learning tight frame, Geophysics, 80(5), V119-V132., doi: 10.1190/geo2014-0396.1
- Zhang, H., Yang, X., and Ma, J., 2020, Can learning from natural image denoising be used for seismic data interpolation?, Geophysics, 85(4), WA115-WA136., doi: 10.1190/geo2019-0243.1
- Zhu, J. Y., Park, T., Isola, P., and Efros, A. A., 2017, Unpaired image-to-image translation using cycle-consistent adversarial networks, Proc. IEEE Int. Conf. Comput. Vis., 2223-2232., doi: 10.1109/ICCV.2017.244