Recognition of geochemical anomalies using a deep variational autoencoder network

Deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level, and non-linear geospatial data and for extracting previously unknown patterns related to geological processes. In thi...

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Bibliographic Details
Published in:Applied geochemistry Vol. 122; p. 104710
Main Authors: Luo, Zijing, Xiong, Yihui, Zuo, Renguang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2020
Subjects:
algorithms
China
copper
Deep learning
ferric oxide
Geochemical mapping
geochemistry
iron
lead
manganese
Mineral exploration
mineralization
mining
probability
spatial data
Variational autoencoder network
zinc
China
Deep learning
Mineral exploration
Geochemical mapping
Variational autoencoder network
ISSN:0883-2927, 1872-9134
Online Access:Get full text
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Summary:Deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level, and non-linear geospatial data and for extracting previously unknown patterns related to geological processes. In this study, a deep variational autoencoder (VAE) network was used to extract features related to mineralization; and these features were then integrated as a anomaly map in support of mineral exploration based on geochemical exploration data, which consist of Cu, Pb, Mn, Zn and Fe2O3. Various experiments were conducted to determine the optimal parameters of the VAE. The structure of the VAE, in which the network depth and number of hidden units were 24–12-3-12-24, was built to recognize geochemical anomalies related to Fe polymetallic mineralization in the southwest Fujian Province, China. The geochemical anomalies recognized by the VAE show a close spatial correlation with known Fe polymetallic deposits. Meanwhile, the areas with high probability are located in or around the Yanshanian intrusions and the contact zones of the Carboniferous–Permian formation and Yanshanian intrusions. These results suggest that the anomalous areas identified by the VAE are meaningful for mineral exploration. •A deep variational autoencoder (VAE) network for multivariate geochemical anomalies recognition is demonstrated.•The reconstruction probability instead of reconstruction error is employed as the anomaly score.•A case study from southwestern Fujian district is conducted.
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ISSN:0883-2927
1872-9134
DOI:10.1016/j.apgeochem.2020.104710