Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest

According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been applied widely in mineralized-anomaly identification. However, they easily ignore spatial coupling information of multi-source ore-indicating factors have lo...

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Bibliographic Details
Published in:	Natural resources research (New York, N.Y.) Vol. 32; no. 1; pp. 1 - 18
Main Authors:	Yang, Na, Zhang, Zhenkai, Yang, Jianhua, Hong, Zenglin
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2023 Springer Nature B.V
Subjects:	Chemistry and Earth Sciences China Computer Science Coupling Earth and Environmental Science Earth Sciences Errors forests Fossil Fuels (incl. Carbon Capture) Geography Geological structures Geology Mathematical Modeling and Industrial Mathematics Mineral deposits Mineral Resources Mineralization Original Paper Physics Reconstruction Statistics for Engineering Sustainable Development China Geological and geochemical data Fengxian area Isolated forest Convolutional sparse autoencoder network Mineralized-anomaly recognition
ISSN:	1520-7439, 1573-8981
Online Access:	Get full text
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