EconPapers    
Economics at your fingertips  
 

Prediction and practical application of bauxite mineralization in Wuzhengdao area, Guizhou, China

Shiqiang Yang, Wu Yang, Tao Cui and Min Zhang

PLOS ONE, 2024, vol. 19, issue 7, 1-23

Abstract: Wu-Zheng-Dao District in China is the world’s most famous mining areas. It hosts several world-class deposits, such as Xinming, Datang and Luolong bauxite deposits. Although this area still has significant potential for the discovery of new deposits, mineral prediction has become increasingly diffcult as the number of shallow deposits diminishes. Therefore, it is necessary to explore new and effective metallogenic prediction methods.Weights of evidence and machine-learning algorithms were used for mineral prospecting in this study. This study used a confusion matrix, receiver operating characteristic (ROC) curve,and prediction efficiency curve to evaluate the prediction results of each machine algorithm. The results showed that 95.9% of the deposits were located in high and distant scenic areas, accounting for 10% of the total area.The prospectivity map of the Wu-Zheng-Dao district shows that the high prospective areas are generally confined to the claystone and carbonatite rocks of the Eastern region, in particular, of the clay layers, and several areas of high prospectivity also occur in the Southern Cross Domain. According to the predicted results, after on-site exploration, design, and construction, Yanfengqian bauxite deposit was discovered, with an average thickness of 1.82 meters; The average content of Al2O3 is 61.24%; The resource amount is 28.9503 million tons.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305917 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 05917&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0305917

DOI: 10.1371/journal.pone.0305917

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().

 
Page updated 2025-05-05
Handle: RePEc:plo:pone00:0305917