Next Article in Journal
A Novel Process for the Synthesis of NaV2O5 Mesocrystals from Alkaline-Stripped Vanadium Solution via the Hydrothermal Hydrogen Reduction Method
Previous Article in Journal
Recovery of Alkali from Bayer Red Mud Using CaO and/or MgO
Article Menu

Export Article

Open AccessArticle

A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

1
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2
National Engineering Research Center of GIS, China University of Geosciences (Wuhan), Wuhan 430074, China
3
Institute of Geophysical and Geochemical Exploration, China Academy of Geological Science, Langfang 065000, China
4
Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
*
Author to whom correspondence should be addressed.
Minerals 2019, 9(5), 270; https://doi.org/10.3390/min9050270
Received: 8 February 2019 / Revised: 24 April 2019 / Accepted: 27 April 2019 / Published: 30 April 2019
  |  
PDF [8533 KB, uploaded 30 April 2019]
  |  

Abstract

The spatial structural patterns of geochemical backgrounds are often ignored in geochemical anomaly recognition, leading to the ineffective recognition of valuable anomalies in geochemical prospecting. In this contribution, a multi-convolutional autoencoder (MCAE) approach is proposed to deal with this issue, which includes three unique steps: (1) a whitening process is used to minimize the correlations among geochemical elements, avoiding the diluting of effective background information embedded in redundant data; (2) the Global Moran’s I index is used to determine the recognition domain of the background spatial structure for each element, and then the domain is used for convolution window size setting in MCAE; and (3) a multi-convolutional autoencoder framework is designed to learn the spatial structural pattern and reconstruct the geochemical background of each element. Finally, the anomaly score at each sampling location is calculated as the difference between the whitened geochemical features and the reconstructed features. This method was applied to the southwestern Fujian Province metalorganic belt in China, using the concentrations of Cu, Mn, Pb, Zn, and Fe2O3 measured from stream sediment samples. The results showed that the recognition domain determination greatly improved the quality of anomaly recognition, and MCAE outperformed several existing methods in all aspects. In particular, the anomalies from MCAE were the most consistent with the known Fe deposits in the area, achieving an area under the curve (AUC) of 0.89 and a forecast area of 17%. View Full-Text
Keywords: multivariate geochemical anomalies; convolutional autoencoder; spatial structure; global Moran’s I multivariate geochemical anomalies; convolutional autoencoder; spatial structure; global Moran’s I
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chen, L.; Guan, Q.; Feng, B.; Yue, H.; Wang, J.; Zhang, F. A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition. Minerals 2019, 9, 270.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Minerals EISSN 2075-163X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top