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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Bibliographic Details
Published in:	Computers & geosciences Vol. 86; pp. 75 - 82
Main Authors:	Xiong, Yihui, Zuo, Renguang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2016
Subjects:	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
Online Access:	Get full text
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