Mineral Potential Mapping Using Principal Coordinate Analysis and Principal Component Analysis in 1:100,000 Scale Porang Sheet, South Khorasan Province

Document Type : Research - Paper

Author

Associate Professor, Dept. of Mining Engineering, Birjand University of Technology, Birjand, Iran

Abstract

The 1:100,000-scale Porang sheet in South Khorasan province is prone to skarn, massive sulfide, and sedimentary mineralization due to the presence of intermediate to ultrabasic volcanic and plutonic rocks and the variety of sedimentary rocks. This paper introduces the Principal Coordinate Analysis (PCoA) method. The PCoA method, along with the Principal Component Analysis (PCA) and Correspondence Analysis (CA) methods, has been used to identify the possible type of mineralization in the study area. Geological and mineralogical data and the analysis results of 25 elements from 314 stream sediment samples, taken from the study area, have been used for this purpose. The results of the data analysis show that the D1 coordinate, PC1 score, and location in the first cluster maps of the samples are most likely related to the mineralization in ultrabasic, basic, and listivinite rocks. After that, the D2 and D3 dimension maps, the PC2 and PC5 score maps, and the sample location map in the fifth cluster related to sedimentary rocks attribute the most probability to sedimentary mineralization, especially of Mn and Fe mineralization types, in the study area. Finally, there is the possibility of skarn and massive sulfide mineralization, whose locations can be predicted by the D4 dimension maps, the PC3 score map, and the sample location maps in second, third, and fourth clusters. Also, the comparison of data analysis results with two multivariate statistical methods shows that by choosing the number of dimensionality reductions, the principal components method can cover more variability than the principal dimensions method. While connecting the principal coordinate maps to the mineralization is easier and more reliable than the principal component score maps. Therefore, the proposal of this paper is the simultaneous use of PCoA and PCA methods to analyze geochemical data in an exploration region.

Keywords

Main Subjects

References

  1. Haldar, S. K. (2018). “Mineral Exploration: Principles and Applications 2nd Edition”. Elsevier, pp. 378.
  2. Talapatra, A. K. (2020). “Geochemical Exploration and Modelling of Concealed Mineral Deposits”. Springer, pp. 210.
  3. Sikakwe, G. U., Nwachukwu, A. N., Uwa, C. U., and Abam Eyong, G. (2020). “Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies”. Environmental Technology & Innovation, 18: 100645.
  4. Iwamori, H., Yoshida, K., Nakamura, H., Kuwatani, T., Hamada, M., Haraguchi, S., and Ueki, K. (2017). “Classification of geochemical data based on multivariate statistical analyses: Complementary roles of cluster, principal component, and independent component analyses”. Geochemistry, Geophysics, Geosystems, 18: 994-1012.
  5. Wang, W., Xie, S., and Carranza, E. J. M. (2023). “Introduction to the thematic collection: applications of innovations in geochemical data analysis”. Geochemistry: Exploration, Environment, Analysis, 23(1). DOI: https://doi.org/10.1144/geochem2022-058.
  6. Rollinson, H., and Pease, V. (2021). “Using Geochemical Data to Understand Geological Processes”. Cambridge University Press, pp. 346.
  7. Su, Q., Yu, H., Xu, X., Chen, B., Yang, L., Fu, T., Liu, W., and Chen, G. (2023). “Using Principal Component Analysis (PCA) Combined with Multivariate Change-Point Analysis to Identify Brine Layers Based on the Geochemistry of the Core Sediment”. Water, 15(10): 1926.
  8. Zuo, R., Wang, J., Xiong, Y., and Wang, Z. (2021). “The processing methods of geochemical exploration data: past, present, and future”. Applied Geochemistry, 132: 105072.
  9. Jimenez-Espinosa, R., Sousa, A. J., and Chica-Olmo, M. (1993). “Identification of geochemical anomalies using principal component analysis and factorial kriging analysis”. Journal of Geochemical Exploration, 46(3): 245-256.
  10. Li, H., Li, Z., Ouyang, Y., Yang, Deng, Y., Jiang, Q., Deng, T., Shang, P., Lin, Y., and Zeng, H. (2022). “Application of principal component analysis and a spectrum-area fractal model to identify geochemical anomalies associated with vanadium mineralization in northeastern Jiangxi Province, South China”. Geochemistry: Exploration, Environment, Analysis, 22(3). DOI: https://doi.org/10.1144/geochem2021-090.
  11. Sadeghi, M., Morris, G. A., Carranza, E. J. M., Laderberger, A., and Andersson, M. (2013). “Rare earth element distribution and mineralization in Sweden: an application of principal component analysis to FOREGS soil geochemistry”. Journal of Geochemical Exploration, 133: 160-175.
  12. Geranian, H., and Carranza, E. J. M. (2022). “Mapping of Regional-scale Multi-element Geochemical Anomalies Using Hierarchical Clustering Algorithms”. Natural Resources Research, 31(4): 1841-1865.
  13. Wojcik, R., Donhauser, J., Frey, B., and Benning, L. G. (2020). “Time since deglaciation and geomorphological disturbances determine the patterns of geochemical, mineralogical and microbial successions in an Icelandic foreland”. Geoderma, 379: 114578.
  14. Wojcik, R., Donhauser, J., Frey, B., Holm, S., Holland, A., Anesio, A. M., Pearce, D. A., Malard, L., Wagner, D., and Benning, L. G. (2019). “Linkages between geochemistry and microbiology in a proglacial terrain in the High Arctic”. Annals of Glaciology, 59(77): 95-110.
  15. Adam, N., Kriete, C., Garbe-Schonberg, D., Gonnella, G., Krause, S., Schippers, A., Kurtz, S., Schwarz-Schampera, U., Han, Y., Indenbirken, D., and Perner, M. (2019). “Microbial Community Compositions and Geochemistry of Sediments with Increasing Distance to the Hydrothermal Vent Outlet in the Kairei Field”. Geomicrobiology Journal, 37(3): 242-254. DOI: 10.1080/01490451.2019.1694107.
  16. Crits-Christoph, A., Robinson, C. K, Barnum, T., Fricke, F., Davila, A., Jedynak, B., McKay, C. P., and DiRuggiero, J. (2013). “Colonization patterns of soil microbial communities in the Atacama Desert”. Microbiome, 1(1): 28.
  17. Gardner, J. P. A., Silva, C. N. S., Norrie, C. R., and Dunphy, B. J. (2021). “Combining genotypic and phenotypic variation in a geospatial framework to identify sources of mussels in northern New Zealand”. Scientifc Reports, 11: 8196.
  18. Campa, M. F., Chen See, J. R., Unverdorben, L. V., Wright, O. G., Roth, K. A., Niles, J. M., Ressler, D., Macatugal, E. M. S., Putt, A. D., Techtmann, S. M., Righetti, T. L., Hazen, T. C., and Lamendella, R. (2022). “Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activit”. Microbiology Spectrum, 10(5): e00770-22.
  19. Zeng, X. C., Wang, G. E. J., Wang, N., Chen, X., Mu, Y., Li, H., Yang, Y., Liu, Y., and Wang, Y. (2016). “Functions and Unique Diversity of Genes and Microorganisms Involved in Arsenite Oxidation from the Tailings of a Realgar Mine”. Applied and Environmental Microbiology, 82: 7019-7029.
  20. نصیری، س.، یوسفی، م.، حائری نسب، م.؛ 1400؛ "بررسی تنوع ژنتیکی کهور پاکستانی (Prosopis juliflora) در استان خوزستان به روش CDDP". مجله پژوهش‌های گیاهی، دوره 34، شماره 1، ص 194-182.
  21. صدیقی، ش.، ایرانبخش، ع.، مهدی حمدی، س. م.، مهرگان، ا.؛ 1400؛ "بررسی روابط فیلوژنی و تنوع ژنتیکی جمعیت‌های Rosmarinus Seidlitzia با استفاده از نشانگر ISSR در برخی مناطق ایران". مجله رستنیها، دوره 22، شماره 1، ص 29-20.
  22. آقانباتی، س. ع.؛ 1393؛ "زمین‌شناسی ایران". انتشارات سازمان زمین‌شناسی ایران، تهران، 640 صفحه.
  23. شهرابی، م.، عربشاهی، ع. ه.، حسینی، م.، شعبانی، ک.؛ 1386؛ "گزارش نقشه زمین‌شناسی 1:100000 پرنگ". سازمان زمین‌شناسی و اکتشافات معدنی کشور، گزارش برگه 8054، 20 صفحه.
  24. Jentzer, M., Fournier, M., Agard, P., Omrani, J., Khatib, M. M., and Whitechurch, H. (2017). “Neogene to Present paleostress field in Eastern Iran (Sistan belt) and implications for regional geodynamics”. Tectonics, 36(2): 321-339.
  25. Wang, Y., Sun, F., Lin, W., and Zhang, S. (2022). “AC-PCoA: Adjustment for confounding factors using principal coordinate analysis”. PLOS Computational Biology, 18(7): e1010184.
  26. Gower, J. C. (2005). “Principal Coordinates Analysis”. In: Encyclopedia of Biostatistics, John Wiley & Sons, 1-5.
  27. Podani, J., and Mikl, I. (2002). “Resemblance Coefficients and the Horseshoe Effect in Principal Coordinates Analysis”. Ecology, 83(12): 3331-3343.
  28. Rencher, A. C., and Christensen, W. F. (2012). “Methods of Multivariate Analysis”. 3rd Edition, Wiley, Hoboken, pp. 800.
  29. Pavoine, S., Dufour, A. B., and Chessel, D. (2004). “From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis”. Journal of Theoretical Biology, 228: 523-537.
  30. Greenacre, M. (2018). “Compositional Data Analysis in Practice”. Chapman and Hall/CRC, pp. 136.
  31. Pawlowsky-Glahn, V., Egozcue, J. J., and Tolosana-Delgado, R. (2015). “Modeling and Analysis of Compositional Data”. Wiley, pp. 256.
  32. گرانیان، ح.؛ 1400؛ "کاربرد تبدیل‌های چند متغیره در تحلیل داده‌های ژئوشیمی محدوده اکتشافی همیچ، استان خراسان جنوبی". نشریه روش‌های تحلیلی و عددی در مهندسی معدن، دوره 11، شماره 27، ص 1-18.
  33. Zhou, S., Zhou, K., Wang, J., Yang, G., and Wang, S. (2017). “Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies”. Frontiers of Earth Science, 12(3): 491-505.
  34. Wang, L., Liu, B., McKinley, J. M., Cooper, M. R., Li, C., Kong, Y., and Shan, M. (2021). “Compositional data analysis of regional geochemical data in the Lhasa area of Tibet, China”. Applied Geochemistry, 135: 105108.
  35. Filzmoser, P., Hron, K., and Reimann, C. (2009). “Principal component analysis for compositional data with outliers”. Environmetrics, 20: 621-632.
  36. Sanford, R. F., Pierson, C. T., and Crovelli, R. A. (1993). “An objective replacement method for censored geochemical data”. Mathematical Geology, 25: 59-80.
  37. Boskabadi, A., Pitcairn, I., Leybourne, M. I., Teagle, D. A. H., Cooper, M. J., Hadizadeh, H., Nasiri Bezenjani, T., and Monazzami Bagherzadeh, R. (2020). “Carbonation of ophiolitic ultramafic rocks: Listvenite formation in the Late Cretaceous ophiolites of eastern Iran”. Lithos, 352-353: 105307.
  38. Vilela, E. F., Guimarães Guilherme, L. R., Silva, C. A., and Zinn, Y. L. (2020). “Trace elements in soils developed from metamorphic ultrabasic rocks in Minas Gerais, Brazil”. Geoderma Regional, 2l: e00279.
  39. [39] Lentz, D. R. (2003). “Geochemistry of Sediments and Sedimentary Rocks: Evolutionary Considerations to Mineral Deposit-Forming Environments”. Geological Assn of Canada, pp. 184.
  40. حسنی‌پاک، ع. ا.؛ 1395؛ "اصول اکتشافات ژئوشیمیایی". انتشارات دانشگاه تهران، 616 صفحه.
  41. Mousivand, F., Rastad, E., Peter, J. M., and Maghfouri, S. (2018). “Metallogeny of volcanogenic massive sulfide deposits of Iran”. Ore Geology Reviews, 95: 974-1007.
  42. Bau, M., Romer, R. L., Lüders, V., and Dulski, P. (2003). “Tracing element sources of hydrothermal mineral deposits: REE and Y distribution and Sr-Nd-Pb isotopes in fluorite from MVT deposits in the Pennine Orefield, England”. Mineralium Deposita, 38: 992-1008.
  43. گرانیان، ح.؛ 1400؛ "شناسایی الگوهای ژئوشیمیایی در محدوده کانی‌زایی خوینرود به روش تحلیل تطبیقی و خوشه‌بندی DENCLUE". نشریه مهندسی معدن، دوره 16، شماره 53، ص 50-32.
Journal of Mineral Resources Engineering
Volume 9, Issue 4 - Serial Number 34
December 2024
Pages 17-38

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History

  • Receive Date: 13 September 2023
  • Revise Date: 30 September 2024
  • Accept Date: 13 May 2024

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