Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation-Reference-Cited by-同舟云学术

Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation

Published:2022 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Saad Omar M.¹^ORCID,Chen Wei²^ORCID,Zhang Fangxue³,Yang Liuqing⁴^ORCID,Zhou Xu⁵^ORCID,Chen Yangkang⁶^ORCID

Affiliation:

1. ENSN Laboratory, Seismology Department, National Research Institute of Astronomy and Geophysics (NRIAG), Helwan 11421, Egypt.

2. Cooperative Innovation Center of Unconventional Oil and Gas (Ministry of Education and Hubei Province) and the Key Laboratory of Exploration Technology for Oil and Gas Resources of Ministry of Education, Yangtze University, Wuhan 430100, China

3. Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China.

4. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102200, China.

5. Craft and Hawkins Department of Petroleum Engineering, Louisiana State University, Baton Rouge, LA 70803 USA.

6. Bureau of Economic Geology, The University of Texas at Austin, University Station, TX 78713 USA.

Funder

National Natural Science Foundation of China NSFC

Open Fund of Cooperative Innovation Center of Unconventional Oil and Gas

Yangtze University Ministry of Education and Hubei Province

Texas Consortium of Computational Seismology TCCS

Texas Seismological Network Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09783461.pdf?arnumber=9783461

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