Quantitative prediction of prospectivity for Pb–Zn deposits in Guangxi (China) by back-propagation neural network and fuzzy weights-of-evidence modelling-Reference-Cited by-同舟云学术

Quantitative prediction of prospectivity for Pb–Zn deposits in Guangxi (China) by back-propagation neural network and fuzzy weights-of-evidence modelling

Published:2022-03-03 Issue:2 Volume:22 Page:
ISSN:1467-7873
Container-title:Geochemistry: Exploration, Environment, Analysis
language:en
Short-container-title:GEEA

Author:

Xie Shuyun¹^ORCID,Huang Ning¹²,Deng Jun³,Wu Songle¹,Zhan Mingguo⁴,Carranza Emmanuel John M.⁵⁶,Zhang Yuepeng¹,Meng Fanxing¹

Affiliation:

1. State Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China

2. Hubei institute of hydrogeology and engineering geology, Jingzhou 434020, China

3. The Guangxi Zhuang Antonomous Region Geological Survey Institute, Nanning, Guangxi 530023, China

4. Guangxi Bureau of Geology & Mineral Resources, Nanning 530023, China

5. Department of Geology, University of the Free State, Bloemfontein 9301, South Africa

6. Geological Sciences, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville 3629, South Africa

Funder

Talent Highland Project of Key Mineral Resources Deep Exploration in Guangxi

provincial entrusted Project under China Geological Survey

Preliminary work project of Bureau of Geological and Mineral Exploration and Development in Guangxi Institute of Geological Survey

Publisher

Geological Society of London

Subject

General Earth and Planetary Sciences,Geochemistry and Petrology,General Environmental Science,General Chemistry

Reference49 articles.

1. A Modified Weights-of-Evidence Method for Regional Mineral Resource Estimation

2. Handbook of Exploration and Environmental Geochemistry;Carranza E.J.M.,2008

3. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm

4. Quantitative Prediction and Evaluation of Mineral Resources Based on GIS: A Case Study in Sanjiang Region, Southwestern China

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