Seismic data interpolation with conditional generative adversarial network in time and frequency domain

插值(计算机图形学) 计算机科学 深度学习 领域(数学分析) 频域 生成语法 对抗制 生成对抗网络 质量(理念) 数据挖掘 地质学 人工智能 地震学 数学 计算机视觉 图像(数学) 哲学 数学分析 认识论
作者
Dekuan Chang,Wei Yang,Xiaoju Yong,H. S Li
标识
DOI:10.1190/segam2019-3210118.1
摘要

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Seismic data interpolation with conditional generative adversarial network in time and frequency domainAuthors: D. K. ChangW. Y. YangX. S. YongH. S LiD. K. ChangResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, W. Y. YangResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, X. S. YongResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this author, and H. S LiResearch institute of petroleum exploration & development-NWGI, PetroChinaSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3210118.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic traces are missing due to limitations in acquisition conditions, bad sectors, etc., which seriously affects the quality of seismic dataset. Seismic data interpolation technology is an effective way to reconstruct missing seismic traces and improve the quality of seismic dataset. In this paper, we propose a method for seismic data interpolation by using the conditional generative adversarial network in time and frequency domain (TF-CGAN). This network consists of two parts, a generation network and a discrimination network. Seismic data and the FFT-transformed data are used for training of the network model to realize dual-domain feature learning. Experimental results show that the TF-CGAN can simultaneously discriminate the mathematical distribution of the interpolated seismic traces in the time and frequency domains, which makes the interpolated seismic traces have the same characteristics with the complete seismic dataset in time and frequency domain.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 1:50 PMLocation: Poster Station 2Presentation Type: PosterKeywords: artificial intelligence, data reconstruction, interpolation, neural networks, processingPermalink: https://doi.org/10.1190/segam2019-3210118.1FiguresReferencesRelatedDetailsCited byGenerative adversarial networks review in earthquake-related engineering fields28 February 2023 | Bulletin of Earthquake Engineering, Vol. 10Unsupervised deep learning for 3D interpolation of highly incomplete dataOmar M. Saad, Sergey Fomel, Raymond Abma, and Yangkang Chen13 December 2022 | GEOPHYSICS, Vol. 88, No. 1Regeneration-Constrained Self-Supervised Seismic Data InterpolationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Big gaps seismic data interpolation using conditional Wasserstein generative adversarial networks with gradient penalty26 October 2021 | Exploration Geophysics, Vol. 53, No. 5Improved Anomalous Amplitude Attenuation Method Based on Deep Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Attention and Hybrid Loss Guided Deep Learning for Consecutively Missing Seismic Data ReconstructionIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Self-Supervised Learning for Efficient Antialiasing Seismic Data InterpolationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Seismic data interpolation using a POCS-guided deep image priorMin Jun Park, Joseph Jennings, Bob Clapp, and Biondo Biondi30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION D. K. Chang, W. Y. Yang, X. S. Yong, and H. S Li, (2019), "Seismic data interpolation with conditional generative adversarial network in time and frequency domain," SEG Technical Program Expanded Abstracts : 2589-2593. https://doi.org/10.1190/segam2019-3210118.1 Plain-Language Summary Keywordsartificial intelligencedata reconstructioninterpolationneural networksprocessingPDF DownloadLoading ...
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
吃猫的鱼完成签到,获得积分10
1秒前
脑洞疼应助润润轩轩采纳,获得10
2秒前
刘文静完成签到,获得积分10
3秒前
Southluuu发布了新的文献求助10
3秒前
chenjyuu发布了新的文献求助10
3秒前
3秒前
粗暴的仙人掌完成签到,获得积分20
3秒前
4秒前
4秒前
4秒前
logic发布了新的文献求助10
4秒前
习习应助生动的雨竹采纳,获得10
4秒前
bo完成签到 ,获得积分10
4秒前
迟大猫应助啵乐乐采纳,获得10
5秒前
安雯完成签到 ,获得积分10
5秒前
HuLL完成签到,获得积分10
5秒前
Yolo完成签到 ,获得积分10
5秒前
难过的慕青完成签到,获得积分10
5秒前
7秒前
7秒前
7秒前
8秒前
无花果应助sunzhiyu233采纳,获得10
8秒前
韭黄完成签到,获得积分20
8秒前
9秒前
诚c发布了新的文献求助10
9秒前
自然秋柳完成签到 ,获得积分10
9秒前
我是老大应助经法采纳,获得10
9秒前
默默的皮牙子应助经法采纳,获得10
9秒前
orixero应助经法采纳,获得10
9秒前
小马甲应助经法采纳,获得10
9秒前
柚子成精应助经法采纳,获得10
10秒前
小蘑菇应助经法采纳,获得10
10秒前
深情安青应助经法采纳,获得10
10秒前
李爱国应助经法采纳,获得10
10秒前
共享精神应助经法采纳,获得10
10秒前
yyyyyy完成签到 ,获得积分10
10秒前
LL完成签到,获得积分10
10秒前
ziyiziyi发布了新的文献求助10
11秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527699
求助须知:如何正确求助?哪些是违规求助? 3107752
关于积分的说明 9286499
捐赠科研通 2805513
什么是DOI,文献DOI怎么找? 1539954
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709759