Stratigraphic constraint for deep learning image segmentation of geological units-Reference-Cited by-同舟云学术

Stratigraphic constraint for deep learning image segmentation of geological units

Published:2023-12-14 Issue: Volume: Page:
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Container-title:Third International Meeting for Applied Geoscience & Energy Expanded Abstracts
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Author:

Boillot Lionel¹,Thouvenot Alexandre¹,Kuhn de Chizelle Julien¹,Guillon Sébastien¹

Affiliation:

1. TotalEnergies

Publisher

Society of Exploration Geophysicists and American Association of Petroleum Geologists

Link

https://library.seg.org/doi/pdf/10.1190/image2023-3911313.1

Reference11 articles.

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3. Brandolin, F., M. Ravasi, and T. Alkhalifah, 2023, PINNslope: Seismic data interpolation and local slope estimation with physics informed neural networks: arxiv:2305.15990.

4. Daw, A., A. Karpatne, W. Watkins, J. Read, and V. Kumar, 2021, Physics-guided neural networks (PGNN) an application in lake temperature modeling: arxiv:1710.11431.

5. Gallon, J., S. Guillon, B. Jobard, H. Barucq, and N. Keskes, 2010, Slimming brick cache strategies for seismic horizon propagation algorithms: IEEE/ EG Symposium on Volume Graphics, VG10, 037–044, doi: 10.2312/VG/VG10/037-044.

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1. Stratigraphic constraint for deep learning image segmentation of geological units;Third International Meeting for Applied Geoscience & Energy Expanded Abstracts;2023-12-14