Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan

Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological da...

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Bibliographic Details
Main Authors: Abdallah M. Mohamed Taha, Yantao Xi, Qingping He, Anqi Hu, Shuangqiao Wang, Xianbin Liu
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Minerals
Subjects:
remote sensing
mineral prospectivity mapping
machine learning
random forest
gold mineralization
Sudan
Online Access:https://www.mdpi.com/2075-163X/13/1/49

Internet

https://www.mdpi.com/2075-163X/13/1/49

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