Deep-learning application of salt geometry detection in deep water Brazil
Author:
Affiliation:
1. ExxonMobil Upstream Research Company
2. ExxonMobil Upstream Business Development Company
3. Formerly at ExxonMobil Upstream Research Company
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Link
https://library.seg.org/doi/pdf/10.1190/image2022-3739378.1
Reference15 articles.
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3. Johnson , J. A. Alahi , and L. Fei-Fei , 2016 , Perceptual losses for real-time style transfer and super-resolution : European Conference on Computer Vision , 694 – 711 .
4. Kaur , H. N. Pham , and S. Fomel , 2019 , Estimating the inverse Hessian for amplitude correction of migrated images using deep learning : 89th Annual International Meeting, SEG , Expanded Abstracts, 2278 – 2282 , doi: 10.1190/segam2019-3207296.1 .
5. Lewis , W. , and D. Vigh , 2017 , Deep learning prior models from seismic images for full-waveform inversion : 87th Annual International Meeting, SEG , Expanded Abstracts, 1512 – 1517 , doi: 10.1190/segam2017-17627643.1 .
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