Attention-Based Two-Stage U-Net Horizon Tracking-Reference-Cited by-同舟云学术

Attention-Based Two-Stage U-Net Horizon Tracking

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Luo Yiliang¹,Zhang Gulan²^ORCID,Li Lei³,Zhang Xudong⁴,Duan Jing¹^ORCID,Li Xiangwen⁵,Li Yong¹^ORCID

Affiliation:

1. School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China

2. School of Geoscience and Technology and the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, China

3. Research and Development Center of BGP, China National Petroleum Corporation, Zhuozhou, China

4. Key Laboratory of Marine Mineral Resources, Ministry of Natural Resources, Guangzhou Marine Geological Survey, Guangzhou, China

5. GRI of BGP, China National Petroleum Corporation, Zhuozhou, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09931711.pdf?arnumber=9931711

Reference23 articles.

1. Horizon picking using two-branch network with spatial and time–frequency features;liao;IEEE Geosci Remote Sens Lett,2021

2. Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network

3. Deep learning for relative geologic time and seismic horizons

4. Sharp U-Net: Depthwise convolutional network for biomedical image segmentation

5. TReC: Transferred ResNet and CBAM for Detecting Brain Diseases

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