Recognition of geochemical anomalies using a deep autoencoder network

In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the tr...

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Vydané v:	Computers & geosciences Ročník 86; s. 75 - 82
Hlavní autori:	Xiong, Yihui, Zuo, Renguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.01.2016
Predmet:	Anomalies Autoencoder network case studies China computers Deposition Errors Geochemical exploration Geochemistry iron learning Mineralization Multivariate geochemical data Networks probability probability distribution Reconstruction Skarn-type iron deposits Training China Skarn-type iron deposits Multivariate geochemical data Autoencoder network Geochemical exploration
ISSN:	0098-3004, 1873-7803
On-line prístup:	Získať plný text
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