登录注册
Quick Links : Mindat手册The Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
主页关于 MindatMindat手册Mindat的历史版权Who We Are联系我们于 Mindat.org刊登广告
捐赠给 MindatCorporate Sponsorship赞助板页已赞助的板页在 Mindat刊登 广告的广告商于 Mindat.org刊登广告
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
搜索矿物的性质搜索矿物的化学Mineral Visual ExplorerAdvanced Locality Search随意显示任何一 种矿物Random Locality使用minID搜索邻近产地Search Articles搜索词汇表更多搜索选项
搜索:
矿物名称:
地区产地名称:
关键字:
 
Mindat手册添加新照片Rate Photos产区编辑报告Coordinate Completion Report添加词汇表项目
Mining Companies统计会员列表Mineral MuseumsClubs & Organizations矿物展及活动The Mindat目录表设备设置The Mineral QuizTime Machine
照片搜索Photo GalleriesSearch by ColorPhoto Colour Explorer今天最新的照片昨天最新的照片用户照片相集过去每日精选照片相集Photography

Wu, Xinming, Geng, Zhicheng, Shi, Yunzhi, Pham, Nam, Fomel, Sergey, Caumon, Guillaume (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, 85 (4) doi:10.1190/geo2019-0375.1

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleBuilding realistic structure models to train convolutional neural networks for seismic structural interpretation
JournalGeophysics
AuthorsWu, XinmingAuthor
Geng, ZhichengAuthor
Shi, YunzhiAuthor
Pham, NamAuthor
Fomel, SergeyAuthor
Caumon, GuillaumeAuthor
Year2020 (July 1)Volume85
Issue4
PublisherSociety of Exploration Geophysicists
DOIdoi:10.1190/geo2019-0375.1Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID915427Long-form Identifiermindat:1:5:915427:2
GUID0
Full ReferenceWu, Xinming, Geng, Zhicheng, Shi, Yunzhi, Pham, Nam, Fomel, Sergey, Caumon, Guillaume (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, 85 (4) doi:10.1190/geo2019-0375.1
Plain TextWu, Xinming, Geng, Zhicheng, Shi, Yunzhi, Pham, Nam, Fomel, Sergey, Caumon, Guillaume (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics, 85 (4) doi:10.1190/geo2019-0375.1
In(2020, July) Geophysics Vol. 85 (4) Society of Exploration Geophysicists

See Also

These are possibly similar items as determined by title/reference text matching only.

Geng, Zhicheng, Wu, Xinming, Shi, Yunzhi, Fomel, Sergey (2020) Deep learning for relative geologic time and seismic horizons. GEOPHYSICS, 85 (4) doi:10.1190/geo2019-0252.1
Geng, Zhicheng, Wu, Xinming, Fomel, Sergey, Chen, Yangkang (2020) Relative time seislet transform. Geophysics, 85 (2) doi:10.1190/geo2019-0212.1
Shi, Yunzhi, Wu, Xinming, Fomel, Sergey (2020) Waveform embedding: Automatic horizon picking with unsupervised deep learning. GEOPHYSICS, 85 (4) doi:10.1190/geo2019-0438.1
Wu, Xinming, Liang, Luming, Shi, Yunzhi, Fomel, Sergey (2019) FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84 (3) doi:10.1190/geo2018-0646.1
Shi, Yunzhi, Wu, Xinming, Fomel, Sergey (2019) SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network. Interpretation, 7. doi:10.1190/int-2018-0235.1
Pham, Nam, Wu, Xinming, Zabihi Naeini, Ehsan (2020) Missing well log prediction using convolutional long short-term memory network. GEOPHYSICS, 85 (4) doi:10.1190/geo2019-0282.1
Wu, Xinming, Liang, Luming, Shi, Yunzhi, Geng, Zhicheng, Fomel, Sergey (2019) Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophysical Journal International, 219 (3) 2097-2109 doi:10.1093/gji/ggz418
Shi, Yunzhi, Wu, Xinming, Fomel, Sergey (2021) Interactively tracking seismic geobodies with a deep-learning flood-filling network. GEOPHYSICS, 86 (1) doi:10.1190/geo2020-0042.1
Kaur, Harpreet, Pham, Nam, Fomel, Sergey (2020) Improving the resolution of migrated images by approximating the inverse Hessian using deep learning. GEOPHYSICS, 85 (4) doi:10.1190/geo2019-0315.1
Di, Haibin, Li, Zhun, Maniar, Hiren, Abubakar, Aria (2020) Seismic stratigraphy interpretation by deep convolutional neural networks: A semisupervised workflow. GEOPHYSICS, 85 (4) doi:10.1190/geo2019-0433.1
Merzlikin, Dmitrii, Fomel, Sergey, Wu, Xinming (2020) Least-squares diffraction imaging using shaping regularization by anisotropic smoothing. GEOPHYSICS, 85 (5) doi:10.1190/geo2019-0741.1
 
and/or  
版权所有© mindat.org1993年至2026年，除了规定的地方。 Mindat.org全赖于全球数千个以上成员和支持者们的参与。
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
隐私政策 - 条款和条款细则 - 联络我们 - Report a bug/vulnerability Current server date and time: 2026.6.5 12:14:08
Go to top of page