Salt3DNet: A Self-Supervised Learning Framework for 3-D Salt Segmentation-Reference-Cited by-同舟云学术

Salt3DNet: A Self-Supervised Learning Framework for 3-D Salt Segmentation

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yang Liuqing¹^ORCID,Fomel Sergey²^ORCID,Wang Shoudong¹^ORCID,Chen Xiaohong¹^ORCID,Saad Omar M.³,Chen Yangkang²^ORCID

Affiliation:

1. State Key Laboratory of Petroleum Resources and Prospecting and the National Engineering Laboratory of Offshore Oil Exploration, China University of Petroleum (Beijing), Beijing, China

2. Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA

3. Division of Physical Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Funder

National Key Research and Development Program of China

Strategic Cooperation Technology Projects of China National Petroleum Corporation (CNPC) and China University of Petroleum (Beijing)

Research and Development Department of China National Petroleum Corporation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10510248.pdf?arnumber=10510248

Reference53 articles.

1. Subsalt imaging for exploration, production, and development: A review

2. A numerically exact local solver applied to salt boundary inversion in seismic full-waveform inversion

3. Application of Envelope in Salt Structure Velocity Building: From Objective Function Construction to the Full-Band Seismic Data Reconstruction

4. Deep-Learning Inversion of Seismic Data

5. Receiver-extension strategy for time-domain full-waveform inversion using a relocalization approach

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