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...

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Bibliographic Details
Published in:	Mathematical geosciences Vol. 54; no. 4; pp. 783 - 806
Main Authors:	Xiong, Yihui, Zuo, Renguang, Luo, Zijing, Wang, Xueqiu
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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