Dickinson, A., White, N. J., and Caulfield, C. P.: Spatial Variation of
Diapycnal Diffusivity Estimated From Seismic Imaging of Internal Wave Field,
Gulf of Mexico, J. Geophys. Res.-Ocean., 122, 9827–9854,
https://doi.org/10.1002/2017JC013352, 2017.
Fortin, W. F., Holbrook, W. S., and Schmitt, R. W.: Seismic estimates of
turbulent diffusivity and evidence of nonlinear internal wave forcing by
geometric resonance in the South China Sea, J. Geophys. Res.-Ocean., 122, 8063–8078, https://doi.org/10.1002/2017JC012690, 2017.
Fortin, W. F. J., Holbrook, W. S., and Schmitt, R. W.: Mapping turbulent
diffusivity associated with oceanic internal lee waves offshore Costa Rica,
Ocean Sci., 12, 601–612, https://doi.org/10.5194/os-12-601-2016, 2016.
Gondara, L.: Medical image denoising using convolutional denoising
autoencoders, in: 2016 IEEE 16th International Conference on Data Mining
Workshops (ICDMW), 241–246, https://doi.org/10.1109/ICDMW.2016.0041, 2016.
Gonella, J. and Michon, D.: Ondes internes profondes rèvèlèes
par sismique rè?exion au sein des masses d'eua en atlantique-est,
Comptes Rendus de l'Académie des Sciences, Série II, 306, 781–787,
1988.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: Proceedings of the IEEE conference on computer vision and
pattern recognition, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016.
Holbrook, W. S., Páramo, P., Pearse, S., and Schmitt, R. W.:
Thermohaline fine structure in an oceanographic front from seismic
reflection profiling, Science, 301, 821–824,
https://doi.org/10.1126/science.1085116, 2003.
Holbrook, W. S., Fer, I., Schmitt, R. W., Lizarralde, D., Klymak, J. M.,
Helfrich, L. C., and Kubichek, R.: Estimating oceanic turbulence dissipation
from seismic images, J. Atmos. Ocean. Technol., 30,
1767–1788, https://doi.org/10.1175/JTECH-D-12-00140.1, 2013.
Hore, A. and Ziou, D.: Image quality metrics: PSNR vs. SSIM, in: 2010 20th
International Conference on Pattern Recognition, 2366–2369,
https://doi.org/10.1109/ICPR.2010.579, 2010.
Huang, G. B. and Babri, H. A.: Upper bounds on the number of hidden neurons
in feedforward networks with arbitrary bounded nonlinear activation
functions, IEEE Transactions on Neural Networks, 9, 224–229,
https://doi.org/10.1109/72.655045, 1998.
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, in: International Conference
on Machine Learning, 448–456, 2015.
Jain, V. and Seung, S.: Natural image denoising with convolutional
networks, in: Advances in Neural Information Processing Systems, 769–776,
2009.
Jun, H., Kim, Y., Shin, J., Shin, C., and Min, D. J.: Laplace-Fourier-domain
elastic full-waveform inversion using time-domain
modelling, Geophysics, 79, R195–R208,
https://doi.org/10.1190/geo2013-0283.1, 2014.
Jun, H., Cho, Y., and Noh, J.: Trans-dimensional Markov chain Monte Carlo
inversion of sound speed and temperature: Application to Yellow Sea
multichannel seismic data, J. Marine Syst., 197, 103180,
https://doi.org/10.1016/j.jmarsys.2019.05.006, 2019.
Jun, H., Jou, H.-T., Kim, C.-H. Lee, S. H., and Kim, H.-J.: DnCNN_seismic_oceanographyso_dncnn_v1.0, https://doi.org/10.5281/zenodo.4020335, last access: 06 November 2020.
Keras: Keras API reference, available at:
https://keras.io/models/sequential/, last access: 6 November 2020.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, International
Conference on Learning Representations, 2015
Klymak, J. M. and Moum, J. N.: Oceanic isopycnal slope spectra. Part I:
Internal waves, J. Phys. Oceanogr., 37, 1215–1231,
https://doi.org/10.1175/JPO3073.1, 2007.
Lefkimmiatis, S.: Non-local color image denoising with convolutional neural
networks, in: Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition, 3587–3596, https://doi.org/10.1109/CVPR.2017.623, 2017.
Li, H., Yang, W., and Yong, X.: Deep learning for ground-roll noise
attenuation, in: SEG Technical Program Expanded Abstracts 2018, 1981–1985,
https://doi.org/10.1190/segam2018-2981295.1, 2018.
Liu, D., Wang, W., Wang, X., Wang, C., Pei, J., and Chen, W.: Poststack
Seismic Data Denoising Based on 3-D Convolutional Neural Network, IEEE
Trans. Geosci. Remote Sens., 58, 1598–1629,
https://doi.org/10.1109/TGRS.2019.2947149, 2020.
McCormack, M. D.: Neural computing in geophysics, The Leading Edge, 10,
11–15, https://doi.org/10.1190/1.1436771, 1991.
McCormack, M. D., Zaucha, D. E., and Dushek, D. W.: First-break refraction
event picking and seismic data trace editing using neural networks,
Geophysics, 58, 67–78, https://doi.org/10.1190/1.1443352, 1993.
Moon, H. J., Kim, H. J., Kim, C. H., Moon, S., Lee, S. h., Kim, J. S., Jeon,
C. K., Lee, G. H., Lee, S. H., Baek, Y., and Jou, H. T.: Imaging the yellow sea
bottom cold water from multichannel seismic data, J. Oceanogr.,
73, 701–709, https://doi.org/10.1007/s10872-017-0426-0, 2017.
Papenberg, C., Klaeschen, D., Krahmann, G., and Hobbs, R. W.: Ocean
temperature and salinity inverted from combined hydrographic and seismic
data, Geophys. Res. Lett., 37, L04601,
https://doi.org/10.1029/2009GL042115, 2010.
Piété, H., Marié, L., Marsset, B., Thomas, Y., and Gutscher, M.
A.: Seismic reflection imaging of shallow oceanographic structures, J. Geophys. Res.-Ocean., 118, 2329–2344,
https://doi.org/10.1002/jgrc.20156, 2013.
Ruddick, B. R.: Seismic Oceanography's Failure to Flourish: A Possible
Solution, J. Geophys. Res.-Ocean., 123, 4–7,
https://doi.org/10.1002/2017JC013736, 2018.
Ruddick, B. R., Song, H., Dong, C., and Pinheiro, L.: Water column seismic
images as maps of temperature gradient, Oceanography, 22, 192–205,
https://doi.org/10.5670/oceanog.2009.19, 2009.
SEG Wiki: AGL Elastic Marmousi, available at:
https://wiki.seg.org/wiki/AGL_Elastic_Marmousi last access: 6 November 2020a.
SEG Wiki: 1994 BP statics benchmark model, available at:
https://wiki.seg.org/wiki/1994_BP_statics_benchmark_model, last access: 22 June
2020b.
Sheen, K. L., White, N. J., Caulfield, C. P., and Hobbs, R. W.: Seismic
imaging of a large horizontal vortex at abyssal depths beneath the
Sub-Antarctic Front, Nat. Geosci., 5, 542–546,
https://doi.org/10.1038/ngeo1502, 2012.
Si, X. and Yuan, Y.: Random noise attenuation based on residual learning of
deep convolutional neural network, In: SEG Technical Program Expanded
Abstracts 2018, 1986–1990, https://doi.org/10.1190/segam2018-2985176.1,
2018.
Tang, Q., Hobbs, R., Zheng, C., Biescas, B., and Caiado, C.: Markov Chain
Monte Carlo inversion of temperature and salinity structure of an internal
solitary wave packet from marine seismic data, J. Geophys.
Res.-Ocean., 121, 3692–3709, https://doi.org/10.1002/2016JC011810,
2016.
Tsuji, T., Noguchi, T., Niino, H., Matsuoka, T., Nakamura, Y., Tokuyama, H.,
Kuramoto, S., and Bangs, N.: Two-dimensional mapping of fine structures in
the Kuroshio Current using seismic reflection data, Geophys. Res.
Lett., 32, L14609, https://doi.org/10.1029/2005GL023095, 2005.
Wang, Y.: Frequencies of the Ricker wavelet, Geophysics, 80, A31–A37,
https://doi.org/10.1190/geo2014-0441.1, 2015.
Wu, X., Liang, L., Shi, Y., and Fomel, S.: FaultSeg3D: Using synthetic data
sets to train an end-to-end convolutional neural network for 3D seismic
fault segmentation, Geophysics, 84, IM35–IM45,
https://doi.org/10.1190/geo2018-0646.1, 2019.
Yang, F. and Ma, J.: Deep-learning inversion: A next-generation seismic
velocity model building method, Geophysics, 84, R583–R599,
https://doi.org/10.1190/geo2018-0249.1, 2019.
Yilmaz, O.: Seismic data analysis: Processing, inversion, and interpretation
of seismic data, second edition, Society of Exploration Geophysicists,
Tulsa, Oklahoma, 2001.
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L.: Beyond a gaussian
denoiser: Residual learning of deep cnn for image denoising, IEEE
Trans. Image Process., 26, 3142–3155,
https://doi.org/10.1109/TIP.2017.2662206, 2017.
Zhao, X., Lu, P., Zhang, Y., Chen, J., and Li, X.: Swell-noise attenuation:
A deep learning approach, The Leading Edge, 38, 934–942,
https://doi.org/10.1190/tle38120934.1, 2019.