Vote for your favorite mineral in #MinCup25! - Topaz vs. Kyanite
It's a battle of high-pressure colourful beauties of #topaz vs #kyanite. Which can stand the heat of round 1?
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

Puzyrev, Vladimir (2019) Deep learning electromagnetic inversion with convolutional neural networks. Geophysical Journal International, 218 (2) 817-832 doi:10.1093/gji/ggz204

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDeep learning electromagnetic inversion with convolutional neural networks
JournalGeophysical Journal International
AuthorsPuzyrev, VladimirAuthor
Year2019 (August 1)Volume218
Issue2
PublisherOxford University Press (OUP)
DOIdoi:10.1093/gji/ggz204Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID892364Long-form Identifiermindat:1:5:892364:0
GUID0
Full ReferencePuzyrev, Vladimir (2019) Deep learning electromagnetic inversion with convolutional neural networks. Geophysical Journal International, 218 (2) 817-832 doi:10.1093/gji/ggz204
Plain TextPuzyrev, Vladimir (2019) Deep learning electromagnetic inversion with convolutional neural networks. Geophysical Journal International, 218 (2) 817-832 doi:10.1093/gji/ggz204
In(2019, August) Geophysical Journal International Vol. 218 (2) Oxford University Press (OUP)

See Also

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

Moghadas, Davood (2020) One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network. Geophysical Journal International, 222 (1) 247-259 doi:10.1093/gji/ggaa161
Puzyrev, Vladimir, Swidinsky, Andrei (2021) Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks. Computers & Geosciences, 149. 104681pp. doi:10.1016/j.cageo.2020.104681
Puzyrev, Vladimir, Torres-Verdín, Carlos, Calo, Victor (2018) Interpretation of deep directional resistivity measurements acquired in high-angle and horizontal wells using 3-D inversion. Geophysical Journal International, 213 (2) 1135-1145 doi:10.1093/gji/ggy047
Puzyrev, Vladimir, Cela, José María (2015) A review of block Krylov subspace methods for multisource electromagnetic modelling. Geophysical Journal International, 202 (2) 1241-1252 doi:10.1093/gji/ggv216
Zhao, Fuxi, Xu, Ying, Zheng, Naiquan, Tu, Zejie, Yang, Fanlin (2025) ARPENN: an improved deep convolutional neural network for bathymetry inversion with integrated physical constraints. Geophysical Journal International, 241 (2). doi:10.1093/gji/ggaf081
Shi, Yu, Zhang, Jifeng, You, Xiran, Ma, Ziben, Li, Jiachen (2024) Transient electromagnetic inversion to image the shallow subsurface based on convolutional bidirectional long short-term memory neural networks. Geophysical Journal International, 239 (1). doi:10.1093/gji/ggae253
Wu, Sihong, Huang, Qinghua, Zhao, Li (2024) Physics-guided deep learning-based inversion for airborne electromagnetic data. Geophysical Journal International, 238 (3) 1774-1789 doi:10.1093/gji/ggae244
Vantassel, Joseph P, Kumar, Krishna, Cox, Brady R (2022) Using convolutional neural networks to develop starting models for near-surface 2-D full waveform inversion. Geophysical Journal International, 231 (1) 72-90 doi:10.1093/gji/ggac179
Gao, Kai, Huang, Lianjie (2019) Acoustic- and elastic-waveform inversion with total generalized p-variation regularization. Geophysical Journal International, 218 (2) 933-957 doi:10.1093/gji/ggz203
Ray, Anandaroop, Myer, David (2019) Bayesian geophysical inversion with trans-dimensional Gaussian process machine learning. Geophysical Journal International, 217 (3) 1706-1726 doi:10.1093/gji/ggz111
Hansen, T M, Minsley, B J (2019) Inversion of airborne EM data with an explicit choice of prior model. Geophysical Journal International, 218 (2) 1348-1366 doi:10.1093/gji/ggz230
 
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 © 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 13, 2025 23:22:33
Go to top of page