Abstract—
The aim of this paper is to carry out a geochemical multivariate analysis on Qolqola gold deposit which is located in Kordestan province of Iran. The analysis and interpretation of geochemical data was carried out based on the results of 543 drilling core samples analyzed for Cu, Pb, W, Mo, Ag, As, Bi, Hg and Au. The purpose of this paper is to use probability plot modeling method for anomaly separation and data classification. For this purpose, three sub-population from Au probability plot modeling was recognized and the thresholds obtained and labeled with the codes 0, 1, and 2 after modeling (code 0: Au < 60 ppb as background, code 1: 60 ppb < Au < 870 ppb as geochemical halo and code 2: Au > 870 ppb as anomaly range). The rearranged data set was used to evaluate discriminant function method. The results were introduced Ag, Hg, Sb, W and Pb as variables related to the mineralization event. The stepwise method was reduced the important variables as Au, Ag, As, W and Bi. Base on the exploration information from the study area, variables such as Ag, As, Sb, Hg, W, Pb were considered as variables were affected by Au mineralization event.
Similar content being viewed by others
REFERENCES
V. Cloutier, R. Lefebvre, R.Therrien and M. M. Savard, “Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of ground water in a sedimentary rock aquifer system,” Journal of Hydrology, 353(34), 294–313 (2008).
R. Ghavami–Riabi, M. M. Seyedrahimi–Niaraq, R. Khalokakaie, and M.R. Hazareh, “U–spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits,” Journal of Geochemical Exploration, 104, 27–33 (2010).
H. E. Hawkes, and J. S. Webb, Geochemistry in Mineral Expl-oration (New York, 1962).
S. M. Haydari, Mineralogy, geochemistry and fabric of gold mineralization in ductile shear zone of Kravian area, MSc thesis, (Tarbiyat Modares University–Iran, 2004)
H. Iwamori, K. Yoshida, H. Nakamura, T. Kuwatani, M. Morihisa Hamada, S. Satoru Haraguchi, and K. Kenta Ueki, “Classification of geochemical data based on multivariate statistical analyses: Complementary roles of cluster, principal component, and independent component analyses,” Geochemistry, Geophysics, Geosystems, 18(3), 994–1012 (2017).
K. G. McQueen, Identifying Geochemical anomalies, Department of Earth and Marine Sciences, ACT 0200– Technical report (Australian National University, 2006)
A. Piña, L. D. Donado, S. Blake, and T. Cramer, “Compositional multivariate statistical analysis of the hydrogeochemical processes in a fractured massif: La Línea tunnel project, Colombia,” Applied Geochemistry, 95, 1–18 (2018).
N. G. Pisias, R. W. Murray, and R. P. Scudder, “Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts,” Geochemistry, Geophysics, Geosystems, 14, 4015–4020 (2013),
T. Ramayah, A. Noor Hazlina, A. H. Hasliza, M. Z. Siti Rohaida, L. May-Chiun,“Discriminant analysis: An illustrated example,” African Journal of Business Management, 4 (9), 1654–1667 (2010).
C. Reimann, and P. Fizmoser, “Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data,” Environ. Geol. 39, 1001–1014 (2000).
C. Reimann, P. Filzmoser, and R. G. Garrett, “Background and threshold: critical comparison of methods of determination,” Science of the Total Environment 346, 1–16 (2005).
H. Rollinson, Using Geochemical Data: Evaluation, Presentation, Interpretation (Longman, Essex, 1993).
ACKNOWLEDGMENTS
We would like to thank Geological Society of Iran (GSI) for permission to use their regional lithogeochemical data in this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Moradpouri, F., Ghavami-Riabi, R. A Multivariate Geochemical Investigation of Borehole Samples for Gold Deposits Exploration. Geochem. Int. 58, 40–48 (2020). https://doi.org/10.1134/S0016702920010103
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0016702920010103