I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
3D convolutional neural Network-based 3D mineral prospectiv..:
Li, Xiaohui
;
Xue, Chen
;
Chen, Yuheng
...
Ore Geology Reviews. 157 (2023) - p. 105444 , 2023
Link:
https://doi.org/10.1016/j.oregeorev.2023.105444
RT Journal T1
3D convolutional neural Network-based 3D mineral prospectivity modeling for targeting concealed mineralization within Chating area, middle-lower Yangtze River metallogenic Belt, China
UL https://suche.suub.uni-bremen.de/peid=cr-10.1016_j.oregeorev.2023.105444&Exemplar=1&LAN=DE A1 Li, Xiaohui A1 Xue, Chen A1 Chen, Yuheng A1 Yuan, Feng A1 Li, Yue A1 Zheng, Chaojie A1 Zhang, Mingming A1 Ge, Can A1 Guo, Dong A1 Lan, Xueyi A1 Tang, Minhui A1 Lu, Sanming PB Elsevier BV YR 2023 SN 0169-1368 JF Ore Geology Reviews VO 157 SP 105444 LK http://dx.doi.org/https://doi.org/10.1016/j.oregeorev.2023.105444 DO https://doi.org/10.1016/j.oregeorev.2023.105444 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)