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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Veröffentlicht in:	Applied geochemistry Jg. 156; S. 105765
Hauptverfasser:	Zhao, Bo, Zhang, Dehui, Tang, Panpan, Luo, Xiaoyan, Wan, Haoming, An, Lin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2023
Schlagworte:	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
Online-Zugang:	Volltext
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