Discrimination of Mineralized Rock Types in a Copper-Rich Volcanogenic Massive Sulfide Deposit Through Fast Independent Component and Factor Analysis

  • Saeid Hajsadeghi
  • Omid AsghariEmail author
  • Mirsaleh Mirmohammadi
  • Seyed Ahmad Meshkani
Original Paper


Multivariate methods are useful for simplifying the interpretation of variables in geochemical data and are widely used to uncover relationships between elements that are associated with geological and mineralization processes. Among these approaches, factor analysis (FA) is one of the most popular, whereas independent component analysis (ICA) has only been employed in a few cases. This study compared the effectiveness of these methods in distinguishing rock types and detecting mineralization signatures based on data on core samples obtained from the Nohkouhi copper deposit. The FA and ICA discriminated four known rock categories, namely barren rhyodacite, mineralized rhyodacite, barren black shale, and mineralized black shale. Stepwise linear discriminant analysis was used to compare the results of the FA and ICA and select components that effectively enable the discrimination of rock types and mineralization. First, rock types were distinguished with reference to the scores calculated via FA and ICA. The results showed that the FA and ICA achieved overall accuracy levels of 94% and 96% in rock-type discrimination, respectively. Second, each rock type was classified as either mineralized or barren. The FA exhibited classification accuracies of 76% for black shales and 70% for rhyodacites, whereas the ICA yielded classification accuracies of 79% and 88% for the two rock types, respectively.


Fast independent component analysis Factor analysis Discriminant analysis Nohkouhi copper deposit 



The authors would like to thank the Zarmesh Group and Amin Karmania Company. We also acknowledge Prof. Carranza, editor-in-chief of Natural Resources Research, for the expert handling of our manuscript. Finally, our gratitude goes to the two anonymous reviewers for their constructive comments.


  1. Afzal, P., Tehrani, M. E., Ghaderi, M., & Hosseini, M. R. (2016). Delineation of supergene enrichment, hypogene and oxidation zones utilizing staged factor analysis and fractal modeling in Takht-e-Gonbad porphyry deposit, SE Iran. Journal of Geochemical Exploration, 161, 119–127.CrossRefGoogle Scholar
  2. Agterberg, F. P. (1974). Automatic contouring of geological maps to detect areas for mineral exploration. Mathematical Geology, 6, 373–395.CrossRefGoogle Scholar
  3. Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44(2), 139–160.Google Scholar
  4. Asadi, H. H., Kianpouryan, S., Lu, Y. J., & McCuaig, T. C. (2014). Exploratory data analysis and C–A fractal model applied in mapping multi-element soil anomalies for drilling: A case study from the Sari Gunay epithermal gold deposit, NW Iran. Journal of Geochemical Exploration, 145, 233–241.CrossRefGoogle Scholar
  5. Asghari, O., & Hezarkhani, A. (2008). Appling discriminant analysis to separate the alteration zones within the Sungun porphyry copper deposit. Journal of Applied Sciences, 24, 4472–4486.CrossRefGoogle Scholar
  6. Bonham-Carter, G. F., & Chung, C. F. (1983). Integration of mineral resource data for Kasmere Lake area, Northwest Manitoba, with emphasis on uranium. Mathematical Geology, 15, 25–45.CrossRefGoogle Scholar
  7. BoroveC, Z. (1996). Evaluation of the concentrations of trace elements in stream sediments by factor and cluster analysis and the sequential extraction procedure. The Science of the Total Environment, 177, 237–250.CrossRefGoogle Scholar
  8. Carranza, E. J. M. (2009). Geochemical anomaly and mineral prospectivity mapping in GIS. In M. Hale (Ed.), Handbook of exploration and environmental geochemistry (Vol. 11). Amsterdam: Elsevier.Google Scholar
  9. Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110(2), 167–185.CrossRefGoogle Scholar
  10. Davis, J. C. (2002). Statistics and data analysis in geology (3rd ed.). New York: Wiley.Google Scholar
  11. De Sá, C. M., Noronha, F., & da Silva, E. F. (2014). Factor analysis characterization of minor element contents in sulfides from Pb–Zn–Cu–Ag hydrothermal vein deposits in Portugal. Ore Geology Reviews, 62, 54–71.CrossRefGoogle Scholar
  12. Dillon, W. R., & Goldstein, M. (1984). Multivariate analysis methods and applications (No. 519.535 D5).Google Scholar
  13. Fedikow, M. A. F., & Turek, A. (1983). The application of stepwise discriminant analysis to geochemical data from the host rocks of the Sullivan Pb–Zn–Ag deposit, Kimberley, BC, Canada. Journal of Geochemical Exploration, 18(3), 231–244.CrossRefGoogle Scholar
  14. Filzmoser, P., Hron, K., Reimann, C., & Garrett, R. (2009). Robust factor analysis for compositional data. Computers & Geosciences, 35(9), 1854–1861.CrossRefGoogle Scholar
  15. Gholami, R., Moradzadeh, A., & Yousefi, M. (2012). Assessing the performance of independent component analysis in remote sensing data processing. Journal of the Indian Society of Remote Sensing, 40(4), 577–588.CrossRefGoogle Scholar
  16. Ghorbani, M. (2013). Economic geology of Iran. Berlin: Springer.CrossRefGoogle Scholar
  17. Hajsadeghi, S., Asghari, O., Mirmohammadi, M., & Meshkani, S. A. (2016). Indirect rock type modeling using geostatistical simulation of independent components in Nohkouhi volcanogenic massive sulfide deposit, Iran. Journal of Geochemical Exploration, 168, 137–149.CrossRefGoogle Scholar
  18. Hajsadeghi, S., Mirmohammadi, M., Asghari, O., & Meshkani, S. A. (2018). Geology and mineralization at the copper-rich volcanogenic massive sulfide deposit in Nohkouhi, Posht-e-Badam block, Central Iran. Ore Geology Reviews, 92, 379–396.CrossRefGoogle Scholar
  19. Harris, D. P. (1965). Multivariate statistical analysis—A decision tool for mineral exploration. In J. C. Dotson & W. C. Peters (Eds.), Symposium on computers and computer applications in mining and exploration (pp. C1–C35). Ariz: College of Mines, University of Arizona, Tucson.Google Scholar
  20. Harris, D. P., & Pan, G. (1999). Mineral favorability mapping: A comparison of artificial neural networks, logistic regression, and discriminant analysis. Natural Resources Research, 8, 93–109.CrossRefGoogle Scholar
  21. Harris, D. P., Zurcher, L., Stanley, M., Marlow, J., & Pan, G. (2003). A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Natural Resources Research, 12, 241–255.CrossRefGoogle Scholar
  22. Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634.CrossRefGoogle Scholar
  23. Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: John Willey & Sons. Inc.CrossRefGoogle Scholar
  24. Hyvärinen, A., Karhunen, J., & Oja, E. (2004). Independent component analysis (Vol. 46). Hoboken: Wiley.Google Scholar
  25. Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4), 411–430.CrossRefGoogle Scholar
  26. Iwamori, H., & Albarède, F. (2008). Decoupled isotopic record of ridge and subduction zone processes in oceanic basalts by independent component analysis. Geochemistry, Geophysics, Geosystems, 9(4), 1–15.CrossRefGoogle Scholar
  27. Iwamori, H., Albaréde, F., & Nakamura, H. (2010). Global structure of mantle isotopic heterogeneity and its implications for mantle differentiation and convection. Earth and Planetary Science Letters, 299(3), 339–351.CrossRefGoogle Scholar
  28. Jang, C. (2010). Applying scores of multivariate statistical analyses to characterize relationships between hydrochemical properties and geological origins of springs in Taiwan. Journal of Geochemical Exploration, 105, 11–18.CrossRefGoogle Scholar
  29. Jutten, C., & Herault, J. (1991). Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process, 24(1), 1–10.CrossRefGoogle Scholar
  30. Krumbein, W. C., & Graybill, F. A. (1965). An introduction to statistical models in geology. McGraw-Hill.Google Scholar
  31. Liu, J., Cheng, Q., & Wang, J. (2015). Identification of geochemical factors in regression to mineralization endogenous variables using structural equation modeling. Journal of Geochemical Exploration, 150, 125–136.CrossRefGoogle Scholar
  32. Liu, Y., Cheng, Q., Xia, Q., & Wang, X. (2014a). Multivariate analysis of stream sediment data from Nanling metallogenic belt, South China. Geochemistry: Exploration, Environment, Analysis, 14(4), 331–340.Google Scholar
  33. Liu, B., Guo, S., Wei, Y., & Zhan, Z. (2014b). A fast independent component analysis algorithm for geochemical anomaly detection and its application to soil geochemistry data processing. Journal of Applied Mathematics, 2014, 1–12.Google Scholar
  34. López, J. M., Borrajo, J. L., García, E. D. M., Arrans, J. R., Estévez, M. C. H., & Castillo, A. S. (2008). Multivariate analysis of contamination in the mining district of Linares (Jaén, Spain). Applied Geochemistry, 23(8), 2324–2336.CrossRefGoogle Scholar
  35. Makvandi, S., Ghasemzadeh-Barvarz, M., Beaudoin, G., Grunsky, E. C., McClenaghan, M. B., Duchesne, C., et al. (2016). Partial least squares-discriminant analysis of trace element compositions of magnetite from various VMS deposit subtypes: Application to mineral exploration. Ore Geology Reviews, 78, 388–408.CrossRefGoogle Scholar
  36. McKinley, J. M., Roberson, S., Cooper, M., & Tolosana-Delgado, R. (2014). Discriminant analysis of Palaeogene basalt lavas, Northern Ireland, using soil geochemistry. In E. Pardo-Igúzquiza, C. Guardiola-Albert, J. Heredia, L. Moreno-Merino, J. Durán, & J. Vargas-Guzmán (Eds.), Mathematics of planet earth Lecture notes in earth system sciences. Berlin: Springer.Google Scholar
  37. Pan, G. C., & Harris, D. P. (2000). Information synthesis for mineral exploration. New York: Oxford University Press Inc.Google Scholar
  38. Prelat, A. E. (1977). Discriminant analysis as a method of predicting mineral occurrence potentials in central Norway. Mathematical Geology, 9, 343–367.CrossRefGoogle Scholar
  39. Reimann, C., Filzmoser, P., & Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 17(3), 185–206.CrossRefGoogle Scholar
  40. Rose, A. W. (1972). Favorability for Cornwall-type magnetite deposits in Pennsylvania using geological, geochemical and geophysical data in a discriminant function. Journal of Geochemical Exploration, 1, 181–194.CrossRefGoogle Scholar
  41. Roshani, P., Mokhtari, A. R., & Tabatabaei, S. H. (2013). Objective based geochemical anomaly detection—Application of discriminant function analysis in anomaly delineation in the Kuh Panj porphyry Cu mineralization (Iran). Journal of Geochemical Exploration, 130, 65–73.CrossRefGoogle Scholar
  42. Rousseeuw, P. J., & Driessen, K. V. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3), 212–223.CrossRefGoogle Scholar
  43. Saager, R., & Esselaar, P. A. (1969). Factor analysis of geochemical data from the basal reef, Orange Free State goldfield, South Africa. Economic Geology, 64(4), 445–451.CrossRefGoogle Scholar
  44. Sahandi, M. R., Soheily, M., Sadeghi, M., Delavar, S. T., & Jafari Rad, A. (2002). Geological Map of Iran, 1:1,000,000. Geological Survey of Iran: Tehran. (unpublished).Google Scholar
  45. Selinus, O. (1983). Factor and discriminant analysis to lithogeochemical prospecting in an area of central Sweden. Journal of Geochemical Exploration, 19(1), 619–642.CrossRefGoogle Scholar
  46. Tibljas, D., Loparic, V., & Belak, M. (2002). Discriminant function analysis of Miocene volcaniclastic rocks from North-Western based geochemical data. Geologia Croatica, 55, 39–44.Google Scholar
  47. Treiblmaier, H., & Filzmoser, P. (2010). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. Information & Management, 47(4), 197–207.CrossRefGoogle Scholar
  48. Vahdati Daneshmand, F., Zohrehbakhsh, A., Djokovic, I., & Dimitrijevic, M. D. (1990). Geology Map of Rafsanjan, Scale 1:250,000. Tehran: Geological Survey of Iran.Google Scholar
  49. Varadanchari, C., & Mukherjee, G. (2004). Discriminant analysis of clay mineral composition. Journal of Clay and Clay Minerals, 52, 311–320.CrossRefGoogle Scholar
  50. Whitehead, R. E. S., & Govett, G. J. S. (1974). Exploration rock geochemistry detection of trace element halos, Heath Steele Mines (N.B., Canada), by discriminant analysis. Journal of Geochemical Exploration, 3, 371–386.CrossRefGoogle Scholar
  51. Yang, J., & Cheng, Q. (2015a). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part I: Simulations. Journal of Geochemical Exploration, 149, 127–135.CrossRefGoogle Scholar
  52. Yang, J., & Cheng, Q. (2015b). A comparative study of independent component analysis with principal component analysis in geological objects identification, Part II: A case study of Pinghe District, Fujian, China. Journal of Geochemical Exploration, 149, 136–146.CrossRefGoogle Scholar
  53. Yasukawa, K., Nakamura, K., Fujinaga, K., Iwamori, H., & Kato, Y. (2016). Tracking the spatiotemporal variations of statistically independent components involving enrichment of rare-earth elements in deep-sea sediments. Scientific Reports, 6, 29603.CrossRefGoogle Scholar
  54. Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environment, Analysis, 14(1), 45–58.Google Scholar
  55. Yu, X., Liu, L., Hu, D., & Wang, Z. (2012). Robust ordinal independent component analysis (ROICA) applied to mineral resources prediction. Journal of Jilin University (Earth Science Edition), 42(3), 872–880.Google Scholar
  56. Yu, X., Liu, S., Ren, J., & Zhang, T. (2007). Robust fast independent component analysis applied to mineral resources prediction. Proceedings of the IAMG, 7, 94–97.Google Scholar
  57. Zhang, T., Yu, X., Liu, L., Yu, X., & Leng, H. (2007). Constrained fast independent component analysis applied to mineral resources prediction. Proceedings of the IAMG, 07, 535–540.Google Scholar
  58. Zuo, R. (2014). Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China. Journal of Geochemical Exploration, 139, 170–176.CrossRefGoogle Scholar
  59. Zuo, R., Xia, Q., & Wang, H. (2013a). Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Applied Geochemistry, 28, 202–211.CrossRefGoogle Scholar
  60. Zuo, R., Xia, Q., & Zhang, D. (2013b). A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry, 33, 165–172.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.School of Mining Engineering, College of EngineeringUniversity of TehranTehranIslamic Republic of Iran
  2. 2.Simulation and Data Processing Laboratory, School of Mining Engineering, College of EngineeringUniversity of TehranTehranIslamic Republic of Iran

Personalised recommendations