气候变化领域集成服务门户(CCPortal): CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer

CCPortal

DOI	10.1109/TGRS.2024.3389780
	CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer
	Chen, Gui; Liu, Yang; Di, Xi; Zhang, Haoran
发表日期	2024
ISSN	0196-2892
EISSN	1558-0644
起始页码	62
卷号	62
英文摘要	To tackle the pressing issue of climate change stemming from carbon emissions, carbon capture and storage (CCS) projects have emerged worldwide, which aim to store carbon dioxide (CO2) produced during industrial production in subsurface geological structures. To ensure the efficacy of these projects, 4-D seismic surveys are conducted to monitor the stored CO2 and identify potential leakage at an early stage. In recent years, deep learning (DL) has been widely employed for seismic data interpretation, which has shown promising results in terms of objectivity and efficiency when compared to manual interpretation. In this study, we address the CO2 monitoring challenge using a 3-D encoder-decoder network with convolutional vision transformer (CvT) called CvTNet, through the supervised learning scheme. By formulating the CO2 monitoring task as an image segmentation problem, we use CvTNet to generate a 3-D CO2 probability image from a 4-D seismic image. CvTNet leverages the CvT module, which provides superior dynamic attention and global context compared to convolutional neural networks. We evaluate the effectiveness of CvTNet on the Sleipner CCS project, using a 4-D seismic image (comprising a 3-D baseline image from 1994 and a 3-D time-lapse image from 2010) and a CO2 probability image from 2010 as the CvTNet training input and label, respectively. We apply the trained model to seismic images from other monitoring years to analyze CO2 plume growth during the Sleipner CCS project. Tests indicate that CvTNet achieves higher CO2 segmentation accuracy than U-Net and can be generalized across other 4-D seismic images.
英文关键词	4-D seismic image; carbon capture and storage (CCS); carbon dioxide (CO2) segmentation; convolutional vision transformer (CvT); deep learning (DL); multihead self-attention (MSA)
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001225891900022
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299620
作者单位	China University of Petroleum; China University of Petroleum; China National Petroleum Corporation; China University of Petroleum
推荐引用方式 GB/T 7714	Chen, Gui,Liu, Yang,Di, Xi,et al. CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer[J],2024,62.
APA	Chen, Gui,Liu, Yang,Di, Xi,&Zhang, Haoran.(2024).CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62.
MLA	Chen, Gui,et al."CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024).