{"title":"FR-UNet:基于特征恢复的地震数据连续缺失轨迹插值 UNet","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":"{\"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}","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}
FR-UNet: A Feature Restoration-Based UNet for Seismic Data Consecutively Missing Trace Interpolation
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.
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.