A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition

Quantification and recognition of geochemical patterns are extremely important for geochemical prospecting and can facilitate a better understanding of regional metallogenesis. Recognition of such patterns with deep learning (DL) algorithms has attracted considerable attention, as these algorithms c...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Mathematical geosciences Ročník 54; číslo 4; s. 783 - 806
Hlavní autoři:	Xiong, Yihui, Zuo, Renguang, Luo, Zijing, Wang, Xueqiu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022 Springer Nature B.V
Témata:	Algorithms Chemistry and Earth Sciences Computer Science Constraints Deep learning Earth and Environmental Science Earth Sciences Feature extraction Fractal analysis Geochemical prospecting Geochemistry Geology Geotechnical Engineering & Applied Earth Sciences Hydrogeology Machine learning Metallogenesis Mineral deposits Mineralization Nonlinear control Optimization Pattern recognition Physics Regularization Statistics for Engineering Tungsten Variational autoencoder Physically constrained deep learning Geochemical patterns Geochemical exploration
ISSN:	1874-8961, 1874-8953
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!