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XRD and NIR Analysis of Oxidation Particles in Dabashan Polymetallic Deposit and Its Significance |
DENG Yong-kang1, 2, CAO Jian-jin1, 2*, DANG Wan-qiang1, 2, WANG Guo-qiang1, 2, LIU Xiang1, 2, LI De-wei1, 2 |
1. School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 510275, China
2. Guangdong Provincial Key Laboratory of Geological Processes and Mineral Resource Survey, Guangzhou 510275, China |
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Abstract XRD and NIR techniques were used to analyze the seven oxide sample particles in the lower sub-group oxidized ore of the Dabaoshan dong Gangling Formation. The first four samples were of the same elevation and the latter three samples were of different elevations. XRD and NIR results show that as the degree of oxidation deepens (04-2→04-3→04-4), the wavelength corresponding to the absorption peak of Al—OH mineral increases continuously (2 160.72→2 163.05→2 200.36 nm). It is indicated that the cationic Al in the mineral is substituted, resulting in an Al-poor phenomenon; and the corresponding peak intensity is from 7.08×10-4, 7.83×10-3 to 6.66×10-2, which indicates that the content of Al—OH minerals is increasing; in addition, the intensity of the absorption peak corresponding to SO2-4 mineral (1 938.80→1 946.94→1 926.47 nm) is from 5.635×10-2, 1.82×10-2 to 1.668×10-2. It is indicated that the content of SO2-4 minerals decreases with the progress of oxidation. Combined with previous studies, we can speculate that the early formation of copper polymetallic sulphide deposits will undergo strong oxidation in the later stage, causing the sulfide ore bodies to oxidize. Oxidation forms a strongly acidic sulfuric acid solution, and the surrounding rock is corroded by a sulfuric acid solution to convert it into loose clay; Sodiumalumite and potassium alumite were found in samples 04-2, 04-3, 13-1. A large number of strontium minerals indicate that the oxidative leaching of the ore is still ongoing; Minerals such as quartz, sericite, calcite, epidote, hornblende, tremolite, phlogopite, chlorite, kaolin, etc. have been discovered by XRD and NIR techniques, which reflect the type of alteration, and the geological features of the area are consistent. At present, near-infrared spectroscopy has been used for alteration mapping in mineral deposit exploration. In this paper, the relationship between the deep oxidation process of the deposit and the cation substitution was discovered by means of spectroscopy, and the interpretation of the genesis of the Dabaoshan deposit was verified by the spectroscopy. The results of this paper show that on the one hand, XRD and NIR can effectively analyze the mineral composition of soil and rock, and provide services for the ore deposit research in this area. On the other hand, NIR can reflect the ion transfer and the sharpness of the peak reflects the crystallization. The intensity of the peak reflects the mineral content, and these unique advantages make it possible to study the oxidation of minerals from a microscopic point of view. However, there is one point that needs to be pointed out. Compared with the research of NIR and X-ray diffraction in other fields, the application of these two technologies in geology needs to be further deepened, including the theoretical basis for the application to geology research and analysis and interpretation of the spectrum, in order to not only analyze the corresponding mineral types by spectroscopy, but also quickly analyze the content of different minerals and different configurations of the same mineral.
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Received: 2018-07-09
Accepted: 2018-11-15
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Corresponding Authors:
CAO Jian-jin
E-mail: eescjj@mail.sysu.edu.cn
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[1] ZHANG Xiong(张 熊). World Non-ferrous Metals(世界有色金属), 2017,2(9):139.
[2] GE Zhao-hua(葛朝华). Mineral Deposits(矿床地质), 1986,5(1):1.
[3] WANG Lan-gen, WANG Yao-wu(王兰根, 王要武). Southern Metals(南方金属), 2012,189(6):31.
[4] DAI Ta-gen, YI Xue-lang, ZHANG De-xian(戴塔根, 尹学朗, 张德贤). Journal of Central South University·Science and Technology(中南大学学报·自然科学版), 2015,46(7):2693.
[5] YAO De-xian(姚德贤). China Geology(中国地质), 1983,3(7):18.
[6] QU Hong-ying, CHEN Mao-hong, YANG Fu-chu, et al(瞿泓滢,陈懋弘,杨富初,等). Acta Petrologic Sinica(岩石学报), 2014,30(1):152.
[7] LI Ying-kui, CAO Jian-jin, WU Zheng-quan, et al(李映葵, 曹建劲,吴政权,等). Spctroscopy and Spectral Analysis(光谱学与光谱分析), 2015,35(1):83.
[8] YING Li-juan, WANG Deng-hong, LI Chao, et al(应立娟,王登红,李 超,等). Earth Science Frontiers(地学前缘),2017,24(5):31.
[9] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍录,赵龙莲,韩东海,等). Near Infrared Spectrum Analysis and Application(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社),2005. 1.
[10] Jerry Workman, Jr Lois Weyer. Practical Guide to Interpretive Near-Infrared Spectroscopy(近红外光谱解析实用指南). Translated by CHU Xiao-li, XU Yu-peng, TIAN Gao-you(褚小立,许育鹏,田高友,译). Beijing: Chemical Industry Press(北京:化学工业出版社),2009. 10.
[11] XIU Lian-cun, ZHENG Zhi-zhong, YU Zheng-kui, et al(修连存,郑志忠,俞正奎,等). Acta Geologica Sinica(地质学报),2007,81(11):1584. |
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