Vote for your favorite mineral in #MinCup25! - Paddlewheelite vs. Mannardite
It's a battle of the tiny as bright green #paddlewheelite goes up against jet black #mannardite. Both are small yet mighty minerals, but only one can win!
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Li, Wenda, Liu, Hong, Wu, Tianqi, Huo, Shoudong (2023) A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-14 doi:10.1109/tgrs.2023.3311788

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network
JournalIEEE Transactions on Geoscience and Remote Sensing
AuthorsLi, WendaAuthor
Liu, HongAuthor
Wu, TianqiAuthor
Huo, ShoudongAuthor
Year2023Volume61
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
DOIdoi:10.1109/tgrs.2023.3311788Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID16857708Long-form Identifiermindat:1:5:16857708:0
GUID0
Full ReferenceLi, Wenda, Liu, Hong, Wu, Tianqi, Huo, Shoudong (2023) A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-14 doi:10.1109/tgrs.2023.3311788
Plain TextLi, Wenda, Liu, Hong, Wu, Tianqi, Huo, Shoudong (2023) A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-14 doi:10.1109/tgrs.2023.3311788
In(2023) IEEE Transactions on Geoscience and Remote Sensing Vol. 61. Institute of Electrical and Electronics Engineers (IEEE)

See Also

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

Li, Wenda, Liu, Hong, Wang, Jian (2022) A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 60. 1-13 doi:10.1109/tgrs.2021.3073001
Dodda, Vineela Chandra, Kuruguntla, Lakshmi, Mandpura, Anup Kumar, Elumalai, Karthikeyan (2023) Simultaneous Seismic Data Denoising and Reconstruction With Attention-Based Wavelet-Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-14 doi:10.1109/tgrs.2023.3267037
Wu, Sihong, Huang, Qinghua, Zhao, Li (2023) Fast Bayesian Inversion of Airborne Electromagnetic Data Based on the Invertible Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-11 doi:10.1109/tgrs.2023.3264777
Li, Shuangliang, Tian, Yugang, Wang, Cheng, Wu, Hongxian, Zheng, Shaolan (2023) Hyperspectral Image Super-Resolution Network Based on Cross-Scale Nonlocal Attention. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-15 doi:10.1109/tgrs.2023.3269074
Wang, Zixu, Wang, Shoudong, Li, Zhichao, Zhou, Chen, Wang, Zhiyong (2024) AVO Uncertainty Inversion Based on Multitask Variational Bayesian Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 62. doi:10.1109/tgrs.2024.3453448
Wang, Zixu, Wang, Shoudong, Zhou, Chen, Cheng, Wanli (2023) AVO Inversion Based on Closed-Loop Multitask Conditional Wasserstein Generative Adversarial Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-13 doi:10.1109/tgrs.2023.3260908
Lu, Xiaochen, Li, Tong, Zhang, Junping, Jia, Fengde (2022) A Novel Unmixing-Based Hypersharpening Method via Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 60. 1-14 doi:10.1109/tgrs.2021.3063105
Jiang, Hairong; Li, Dengao; Zhao, Jumin; Mu, Zehui; Wu, Fanming (2025) LMCNN: A Lightweight Attention-Based Modern Convolutional Neural Network Design for Runoff Prediction. IEEE Transactions on Geoscience and Remote Sensing, 63. doi:10.1109/tgrs.2025.3590720
Dong, Zhipeng, Liu, Yanxiong, Wang, Yanli, Feng, Yikai, Chen, Yilan, Wang, Yanhong (2023) Enteromorpha Prolifera Detection in High-Resolution Remote Sensing Imagery Based on Boundary-Assisted Dual-Path Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-15 doi:10.1109/tgrs.2023.3326500
Wang, Jun, Zhou, Zhiwen, Meng, Xiaohong, Liu, Yandong, Fang, Yuan (2023) A Novel Method for Eliminating the Strip-Shaped Interferences in Aeromagnetic Anomaly Based on Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-11 doi:10.1109/tgrs.2023.3239345
Guan, Renchu, Wang, Mingming, Bruzzone, Lorenzo, Zhao, Haishi, Yang, Chen (2023) Lightweight Attention Network for Very High-Resolution Image Semantic Segmentation. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-14 doi:10.1109/tgrs.2023.3272614
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
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.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 16, 2025 12:46:03
Go to top of page