Identification of geochemical anomalies associated with Sn-W mineralization in the Dong Van region, North-Eastern Vietnam, using statistical methods
https://doi.org/10.17073/2500-0632-2022-2-100-110
Abstract
Sn-W multimetal mineralization in the Dong Van region, North-Eastern Vietnam was studied using statistical and multivariate approaches based on 890 samples of stream bottom sediments collected for assaying for 27 elements. The findings of frequency analysis demonstrated that Pb, As, Bi, Li, Sn, W, Ta, Ce, Ag, Sb, and Be have close ties with multimetal ores, implying that these elements can be used as prospecting indicators for multimetal mineralization. In addition, correlation matrix and dendrogram studies were also applied to subdivide the elements in the stream bottom sediment samples assays into two groups: associated with multimetal mineralization (Be-Sn-W-Bi, and, to a lesser extent, Li-Pb sub-groups) and not associated with the mineralization: (As-Cd-Sc-Cr-Ce-La, Co-Ni-V, and Ga-Ge-Ba sub-groups). Sn and W were found to be the best indicator elements for the mineralization, according to the findings of geochemical modeling and location of their anomalies in the region. Furthermore, extensive Sn and W anomalies were identified in the Dong Van region (using threshold values (mean ± 3 STD), providing the most important indications for multimetal mineralization prospecting in the region. The studies also suggest genetic ties between the region’s multimetal mineralization and the northwest-southeast fault system and concealed granitoid blocks. Finally, the performed statistical analyses (with the use of threshold values) of stream bottom sediments assays allowed revealing indicator elements and their geochemical anomalies and using them as an effective tool in further prospecting and exploration for multimetal mineralization in the region.
About the Author
K. T. Hung
Hanoi University of Mining and Geology
Viet Nam
Viet Nam
Khuong The Hung – Dr. Sci. (Earth Sci.), Lecturer, Department of Prospecting and Exploration Geology
Hanoi
References
1. Tri T. V., Khuc V. (eds.). Geology and Earth Resources of Vietnam. General Department of Geology and Minerals of Vietnam: Publishing House for Science and Technology. 2011. 645 p.
2. Hung K. T., Sang P. N., Phuong N. et al. Statistical evaluation of the geochemical data for prospecting complex mineralization in the Suoi Thau-Sang Than region, Northeast Vietnam. Geology, Geophysics and Environment. 2020;46(4):285–299. https://doi.org/10.7494/geol.2020.46.4.285
3. Carranza E. J. M. Usefulness of stream order to detect stream sediment geochemical anomalies. Geochemistry: Exploration, Environment, Analysis. 2005;4(4):341–352. https://doi.org/10.1144/1467-7873/03-040
4. Cheng Q., Jing L., Panahi A. Principal component analysis with optimum order sample correlation coefficient for image enhancement. International Journal of Remote Sensing. 2006;27(16):3387–3401. https://doi.org/10.1080/01431160600606882
5. Reimann C., Filzmoser P., Garrett R. G., Dutter R. Statistical Data Analysis Explained: Applied Environmental Statistics with R. Wiley, Chichester, UK; 2008. 362 p. https://doi.org/10.1002/9780470987605
6. Cheng Q., Bonham-Carter G., Wang W., Zhang S., Li W., Qinglin X. A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers and Geosciences. 2011;37(5):662–669. https://doi.org/10.1016/j.cageo.2010.11.001
7. Zuo R. Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China. Journal of Geochemical Exploration. 214;139:170–176. https://doi.org/10.1016/j.gexplo.2013.08.013
8. Aitchison J. The Statistical Analysis of Compositional Data. Chapman & Hall, London; 1986. 416 p. https://doi.org/10.1002/bimj.4710300705
9. Tukey J. W. Exploratory data analysis. Reading, Massachusetts: Addison-Wesley Publishing Company; 1977. 688 p. https://doi.org/10.1177/014662167800200115
10. Sun X., Deng J., Gong Q. et al. Kohonen neural network and factor analysis based approach to geochemical data pattern recognition. Journal of Geochemical Exploration. 2009;103(1):6–16. https://doi.org/10.1016/j.gexplo.2009.04.002
11. Mandelbrot B. B. The fractal geometry of nature. Freeman, San Francisco; 1983. 468 p.
12. Agterberg F. P. Multifractal modeling of the sizes and grades of giant and supergiant deposits. International Geology Review. 1995;37(1):1–8. https://doi.org/10.1080/00206819509465388
13. Wang Q. F., Deng J., Zhao J. et al. Tonnage-cutoff model and average grade-cutoff model for a single ore deposit. Ore Geology Reviews. 2010;38(1–2):113–120. https://doi.org/10.1016/j.oregeorev.2010.07.003
14. Yang L. Q., Wang Q. F., Liu X. F. Correlation between mineralization intensity and fluid-rock reaction in the Xinli gold deposit, Jiaodong Peninsula, China: constraints from petrographic and statistical approaches. Ore Geology Reviews. 2015;71:29–39. https://doi.org/10.1016/j.oregeorev.2015.04.005
15. Reimann C., Filzmoser P., Garrett R. G. Background and threshold: critical comparison of methods of determination. Science of the Total Environment. 2005;346(1–3):1–16. https://doi.org/10.1016/j.scitotenv.2004.11.023
16. Cheng Q. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews. 2007;32(1–2):314–324. https://doi.org/10.1016/j.oregeorev.2006.10.002
17. Sun X., Gong Q., Wang Q. et al. Application of local singularity model to delineate geochemical anomalies in Xiong’ershan gold and molybdenum ore district,Western Henan province, China. Journal of Geochemical Exploration. 2010;107(1):21–29. https://doi.org/10.1016/j.gexplo.2010.06.001
18. Zuo R., Xia Q., Zhang D. A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry. 2013;33:165–172. https://doi.org/10.1016/j.apgeochem.2013.02.009
19. Graedel T. E., Harper E. M., Nassar N. T., Reck B. K. On the materials basis of modern society. Proceedings of the National Academy of Sciences. 2013;112(20):6295–6300. https://doi.org/10.1073/pnas.1312752110
20. Dovzhikov A. E., My B. P., Vasilevskaya E. D. et al. Geology of North Vietnam. Hanoi: Science and Technology Publishing House; 1965. 650 p. (In Russ.)
21. Tinh H. X. (ed.). Report on results of geological mapping and mineral investigation of Bao Lac sheet at 1:200.000 scale. Geological Department of Vietnam, Ha Noi; 1976. (In Vietnamese)
22. Truyen M. T. (ed.). Report on results of geological mapping and mineral investigation of Bao Lac sheet at 1:50.000 scale. Geological Department of Vietnam, Ha Noi; 1977. (In Vietnamese)
23. Tri T. V. (ed.). Report on results of geological and mineral potential mapping of Bao Lac-Bac Quang-Ma Quang at 1:200.000 scale. Geological Department of Vietnam, Ha Noi; 2000 (In Vietnamese)
24. Izokh A. E., Polyakov G. V., Hoa T. T., Balykin P. A., Phuong N. T. Permian–Triassic ultramafic–mafic magmatism of Northern Vietnam and Southern China as expression of plume magmatism. In: 32nd International Geological Congress. 2005;46:942–951. (In Russ.)
25. Hoa T. T., Anh T. T., Phuong N. T. et al. Permo-Triassic intermediate–felsic magmatism of the Truong Son belt, eastern margin of Indochina. ComptesRendus Geoscience. 2008;340(1–2):112–126. https://doi.org/10.1016/j.crte.2007.12.002
26. Polyakov G. V., Shelepaev R. A., Hoa T. T. et al. The Nui Chua layered peridotitegabbro complex asmanifestation of Permo-Triassic mantle plume in northern Vietnam. Russian Geology and Geophysics. 2009;50(6):501–516. https://doi.org/10.1016/j.rgg.2008.10.002
27. Vladimirov A. G., Balykin P. A., Anh P. L. et al. The Khao Que-Tam Tao gabbro-granite massif, Northern Vietnam: a petrological indicator of the Emeishan plume. Russian Journal of Pacific Geology. 2012;6:395–411. https://doi.org/10.1134/S1819714012050065
28. Hai T. T., Thanh N. X., Halpin J. A., Zaw K. The occurrence of ophiolite-style assemblages along Sino-Vietnam border, Northeastern Vietnam and its implication to the tectonic evolution of Northeastern Indochina. In: Proceedings of the International Conference on Geology. Geotechnology and Mineral Resources of Indochina (GEOINDO 2011), KhonKaen. 2011. Pp. 479–488.
29. Thang P. V. (ed.). Report on the results of prospecting Sn-W mineralization and accompaning minerals in the northwestern Ha Giang area. Geological Department of Vietnam, Ha Noi; 1988. (In Vietnamese)
30. Reimann C., Filzmoser P. Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environmental Geology. 2000;39:1001–1014. https://doi.org/10.1007/s002549900081
31. Chakravarti R., Laha G., Roy J. Handbook of Methods of Applied Statistics. Volume I. John Wiley and Sons; 1967. 160 p.
32. Egozcue J. J., Pawlowsky-Glahn V., Mateu-Figueras G., Barceló-Vidal C. Isometric logratio transformations for compositional data analysis. Mathematical Geology. 2003;35:279–300. https://doi.org/10.1023/A:1023818214614
33. Carranza E. J. M. Analysis and mapping of geochemical anomalies using logratiotransformed stream sediment data with censored values. Journal of Geochemical Exploration. 2011;110(2):167–185. https://doi.org/10.1016/j.gexplo.2011.05.007
34. Ward J. H. Jr. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 1963;58:236–244. https://doi.org/10.1080/01621459.1963.10500845
35. Robertson G. P. GS+: Geostatistics for the Environmental Sciences. Gamma Design Software, Plainwell: Michigan USA; 2008. URL: https://softbooks.pl/gammadesign/files/download/gsplususerguide.pdf
36. Fortescue J. A. C. Landscape geochemistry-retrospect and prospect–1990. Applied Geochemistry. 1992;7(1):1–53. https://doi.org/10.1016/0883-2927(92)90012-R
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For citations:
Hung K.T. Identification of geochemical anomalies associated with Sn-W mineralization in the Dong Van region, North-Eastern Vietnam, using statistical methods. Mining Science and Technology (Russia). 2022;7(2):100-110. https://doi.org/10.17073/2500-0632-2022-2-100-110
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