Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification

Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification

Abstract Deep autoencoder (AE) networks show a powerful ability for geochemical anomaly identification. Because of little contribution to the AE network, small probability samples (again, please check this) having comparatively high reconstructed errors can be recognized by the trained model as anom...

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Bibliographic Details
Main Authors:	Bin Feng, Lirong Chen, Yongyang Xu, Yu Zhang
Format:	Article
Language:	English
Published:	American Geophysical Union (AGU) 2022-11-01
Series:	Earth and Space Science
Subjects:	geochemical anomaly identification autoencoder feature combination unsupervised learning spatial feature extraction
Online Access:	https://doi.org/10.1029/2022EA002626

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