Deep learning-based Vz-noise attenuation for OBS data
Author:
Affiliation:
1. Shearwater Geoservices
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Link
https://library.seg.org/doi/pdf/10.1190/image2023-3910189.1
Reference7 articles.
1. He, K., X. Zhang, S. Ren, and J. Sun, 2016, Deep residual learning for image recognition: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
2. Kumar, A., G. Hampson, and T. Rayment, 2021, Simultaneous up-down separation and Vz denoise using joint sparsity recovery: 82nd Annual International Conference and Exhibition, EAGE, Extended Abstracts, doi: 10.3997/2214-4609.202112695.
3. Paffenholz, J., P. Docherty, R. Shurtleff, and D. Hays, 2006, Shear wave noise on OBS Vz data — Part II elastic modeling of scatterers in the seabed: 68th Annual International Conference and Exhibition, EAGE, Extended Abstracts, doi: 10.3997/2214-4609.201402228.
4. Perrone, F., and S. Grion, 2019, Z-noise attenuation using Gaussian mixtures models and unsupervised learning: 81st Annual International Conference and Exhibition, EAGE, Extended Abstracts, doi: 10.3997/2214-4609.201901347.
5. Poole, G., L. Casasanta, and S. Grion, 2012, Sparse τ-p Z-noise attenuation for ocean-bottom data: 82nd Annual International Meeting, SEG, Expanded Abstracts, doi: 10.1190/segam2012-0679.1.
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