Complete sequence stratigraphy from seismic optical flow without human labeling
Author:
Affiliation:
1. Schlumberger
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2020-3427292.1
Reference7 articles.
1. Sequence Stratigraphy: Guidelines for a Standard Methodology
2. Dawes, R. L., and C. D. Dawes, 2013, https://commons.wvc.edu/rdawes/Basics/stratigraphy.html
3. Di, H., Z. Li, H. Maniar, and A. Abubakar, 2019, Seismic stratigraphy interpretation via deep convolutional neural networks: 89th Annual International Meeting, SEG, Expanded Abstracts, 2358–2362, doi: 10.1190/segam2019-3214745.1.
4. Fisher, P., A. Dosovitskiy, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Smagt, D. Cremers, and T. Brox, 2015, Flownet: Learning Optical Flow with Convolutional Networks: IEEE International Conference on Computer Vision (ICCV).
5. Geng, Z., X. Wu, Y. Shi, and S. Fomel, 2019, Relative geologic time estimation using a deep convolutional neural network: 89th Annual International Meeting, SEG, Expanded Abstracts, 2238–2242, doi: 10.1190/segam2019-3214459.1.
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