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| DOI | 10.1109/TGRS.2024.3389780 |
| CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer | |
| 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
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/299619 |
| 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 | . CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer[J],2024,62. |
| APA | (2024).CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62. |
| MLA | "CO2Seg: Automatic CO2 Segmentation From 4-D Seismic Image Using Convolutional Vision Transformer".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024). |
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