Machine Learning and EPCA Methods for Extracting Lithology–Alteration Multi-Source Geological Elements: A Case Study in the Mining Evaluation of Porphyry Copper Ores in the Gondwana Metallogenic Belt

Machine Learning and EPCA Methods for Extracting Lithology–Alteration Multi-Source Geological Elements: A Case Study in the Mining Evaluation of Porphyry Copper Ores in the Gondwana Metallogenic Belt

The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of the datasets used for mi...

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Bibliographic Details
Main Authors: Chunhui Liu, Xingyu Liu, Man Hou, Sensen Wu, Luoqi Wang, Jie Feng, Chunxia Qiu
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Minerals
Subjects:
Gondwana metallogenic belt
porphyry copper mining evaluation model
spectral feature enhancement
principal component analysis
multi-source geological elements
Online Access:https://www.mdpi.com/2075-163X/13/7/858

Internet

https://www.mdpi.com/2075-163X/13/7/858

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