FR-UNet: A Feature Restoration-Based UNet for Seismic Data Consecutively Missing Trace Interpolation

IF 7.5 1区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yupeng Tian;Lihua Fu;Wenqian Fang;Tao Li
{"title":"FR-UNet: A Feature Restoration-Based UNet for Seismic Data Consecutively Missing Trace Interpolation","authors":"Yupeng Tian;Lihua Fu;Wenqian Fang;Tao Li","doi":"10.1109/TGRS.2025.3531934","DOIUrl":null,"url":null,"abstract":"Convolutional neural network (CNN) is widely used for seismic data recovery and has demonstrated remarkable performance in reconstructing irregularly and regularly sampled seismic data. However, when it comes to recovering consecutively missing traces, CNN encounters difficulties in interpolating large gaps from the surrounding neighborhoods, due to the local property of convolution operator. Excessive missing entries existed in feature maps result in incomplete interpolation results. Thus, we propose a feature restoration-based UNet (FR-UNet) to improve the quality of reconstruction by restoring feature maps. In FR-UNet, we integrate feature recovering through the implementation of an attention transfer module (ATM). This module learns an attention score map from the high-level feature map of UNet, providing guidance for repairing the adjacent low-level feature map. Moreover, to ensure the integrity and precision of the highest level feature map, we utilize partial convolution (PConv) as a replacement for conventional convolution (CConv). Experimental results on synthetic and field data demonstrate that our network generates more accurate results for recovering large gaps through feature restoration.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-10"},"PeriodicalIF":7.5000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Geoscience and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10847732/","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Convolutional neural network (CNN) is widely used for seismic data recovery and has demonstrated remarkable performance in reconstructing irregularly and regularly sampled seismic data. However, when it comes to recovering consecutively missing traces, CNN encounters difficulties in interpolating large gaps from the surrounding neighborhoods, due to the local property of convolution operator. Excessive missing entries existed in feature maps result in incomplete interpolation results. Thus, we propose a feature restoration-based UNet (FR-UNet) to improve the quality of reconstruction by restoring feature maps. In FR-UNet, we integrate feature recovering through the implementation of an attention transfer module (ATM). This module learns an attention score map from the high-level feature map of UNet, providing guidance for repairing the adjacent low-level feature map. Moreover, to ensure the integrity and precision of the highest level feature map, we utilize partial convolution (PConv) as a replacement for conventional convolution (CConv). Experimental results on synthetic and field data demonstrate that our network generates more accurate results for recovering large gaps through feature restoration.
FR-UNet:基于特征恢复的地震数据连续缺失轨迹插值 UNet
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing 工程技术-地球化学与地球物理
CiteScore
11.50
自引率
28.00%
发文量
1912
审稿时长
4.0 months
期刊介绍: IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信