Deep learning in salt interpretation from R&D to deployment: Challenges and lessons learned
Author:
Affiliation:
1. Shell Global Solutions
2. Shell International Exploration and Production Company
3. University of Houston
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Link
https://library.seg.org/doi/pdf/10.1190/image2022-3751725.1
Reference7 articles.
1. Gala, A., Z. Li, E. Alkan, P. Devarakota, and G. Madiba, 2022, Towards Continual AI for salt interpretation using Incremental learning, to appear in SEG Workshop in Applications of ML and AI in Geophysics.
2. Pseudo three-dimensional deep learning approach for top and bottom of salt detection
3. Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
4. Regularization strategies for deep-learning-based salt model building
5. SaltNet: A production-scale deep learning pipeline for automated salt model building
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