Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China

This study has developed a novel variational autoencoder architecture by incorporating the spectrum separable module, termed SSM-VAE, so as to recognize the multi-mineral-species geochemical patterns over the Shangluo district, central China, and then facilitate a better understanding of the regiona...

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Vydané v:	Applied geochemistry Ročník 156; s. 105765
Hlavní autori:	Zhao, Bo, Zhang, Dehui, Tang, Panpan, Luo, Xiaoyan, Wan, Haoming, An, Lin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.09.2023
Predmet:	algorithms case studies China Deep learning factor analysis geochemistry Geology mineralization Multivariate anomaly Spectrum separable module China Deep learning Multivariate anomaly Geology Spectrum separable module
ISSN:	0883-2927, 1872-9134
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