Paper
15 November 2023 A study of metallogenic potential index of ores from the Bayan Obo Nb-Fe-REE deposit and its indication of geometallurgy and remote sensing exploration
Chi Zhang, Xuefeng Zhu, Lina Lu, Tao He
Author Affiliations +
Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128151H (2023) https://doi.org/10.1117/12.3011219
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China
Abstract
Rare earth resources are of great economic and strategic importance. The Bayan Obo, as the largest Nb-Fe-REE deposit in the world, have attracted much attention in mining field. This paper provides two index parameters of ore that can be used to quickly distinguish the ore types with exploitation value or industrial significance, which is helpful for geometallurgy and remote sensing prospecting. Seven ore types from the main and eastern orebodies of Bayan Obo deposits were collected for this study. The composition of mineral and element of each sample was acquired by TESCAN Integrated Mineral Analyzer (TIMA). Method of principal component analysis (PCA) was employed to analyze the compositional data. Metallogenic potential index of minerals and elements (MPIM and MPIE) were defined to make quantitative appraisal on the resource potentiality of Bayan Obo deposit. The ore types with a higher index are thought to be rich in REE. The results show that MPIM and MPIE are helpful to judge the metallogenic potential and mineral exploration in Bayan Obo deposit. The research is expected to be applied to the fields of geometallurgy and remote sensing exploration.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Chi Zhang, Xuefeng Zhu, Lina Lu, and Tao He "A study of metallogenic potential index of ores from the Bayan Obo Nb-Fe-REE deposit and its indication of geometallurgy and remote sensing exploration", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128151H (15 November 2023); https://doi.org/10.1117/12.3011219
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KEYWORDS
Minerals

Chemical elements

Remote sensing

Iron

Principal component analysis

Chemical composition

Fluorite

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