Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder

Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical exploration data typically contain a proportion of large variations and missing values, which motivated us to construct a network architecture optimiz...

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Veröffentlicht in:	Mathematical geosciences Jg. 54; H. 3; S. 623 - 644
Hauptverfasser:	Xiong, Yihui, Zuo, Renguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022 Springer Nature B.V
Schlagworte:	Anomalies Artificial neural networks Chemistry and Earth Sciences Comparative analysis Comparative studies Computer architecture Computer Science Convolution Corruption Earth and Environmental Science Earth Sciences Errors Exploration Feature extraction Geochemistry Geotechnical Engineering & Applied Earth Sciences Hydrogeology Iron Kernels Machine learning Mineralization Missing data Multivariate analysis Neural networks Noise reduction Physics Reconstruction Robustness Sensitivity Spatial distribution Special Issue Statistics for Engineering Deep learning Geochemical anomalies Stacked convolutional denoising autoencoders Geochemical exploration
ISSN:	1874-8961, 1874-8953
Online-Zugang:	Volltext
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