Abstract
Geochemical exploration is mainly utilized for prospecting of economically viable mineralization. Several methods have been introduced for identifying the potential mineralization based on surface geochemical data; however, based on these methods, the definite presence of mineralization in the region cannot be ascertained. Therefore, in this study, a combination of the core drilling analysis with the surface geochemical data was used in order to determine the actual position of the porphyry Cu mineralization. To achieve this objective, multivariate statistical method of multiple linear regression was applied. So, the regression equation is calculated based on the mean of Cu in sample of core drillings and elemental concentrations at surface geochemical rock samples. Predicting regression model has shown coefficient of determination (R 2 = 83 %). The model validity has also been checked through bootstrapping technique, which has demonstrated that the model is valid, with a 95 % confidence level as well. The result of multiple stepwise linear regression model showed that this method could draw the line of the best fit on the rock sample data in order to obtain the positions of mineralization underneath. The result obtained by this method was used to compare the distribution of Cu and Mo at surface samples with the boundary of economic zone in the core drillings, and the results are consistent and elongated northwest–southeast in diorite and quartz-diorite rock units.
Similar content being viewed by others
References
Brown A.A., Olesen B.L.: Condensing multi-element reconnaissance geochemical data from south Greenland using empirical discriminant analysis. J. Geochem. Explor. 21, 395–404 (1984)
Cheng Q., Agterberg F.P., Ballantyne S.B.: The separation of geochemical anomalies from background by fractal methods. J. Geochem. Explor. 51, 109–130 (1994)
Ghavami-Riabi R., Seyedrahimi-Niaraq M.M., Khalokakaie R., Hazareh M.R.: U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. J. Geochem. Explor. 104, 27–33 (2010)
Mosteller F., Tukey J.W.: Data Analysis and Regression. Addison-Wesley, Reading (1977)
Reimann, C., Filzmoser, P., Garrett, R., Dutter, G.: Statistical Data Analysis Explained: Applied Environmental Statistics with R. Wiley, New York, pp. 249–254 (2008)
Johnson R.A., Wichern D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River (1982)
Davis, J.C.: Statistics and Data Analysis in Geology, 3rd edn. Wiley, New York (2002)
Neter J., Wassermann W., Kutner M.H.: Applied Linear Regression Model. Homewood, Irwin (1989)
Hair J.F., Anderson R.E., Tatham R.L., Black W.C.: Multivariate Data Analysis. University of New South Wales, New South Wales (1998)
Size W.B.: Use and Abuse of Statistical Methods in Earth Sciences. New York, Oxford (1986)
Seber George A.F., Lee Alan J.: Linear Regression Analysis. Wiley, New Jersey (2003)
Wilkinson L.: Test of Significance in Stepwise Regression. Psychol. Bull. 86, 168–174 (1975)
Montgomery D.C., Peck E.A., Vining G.G.: Introduction to Linear Regression Analysis. wiley, New Jersey (2012)
Khosravi, A.: Statistical Geological and Alteration Map of Kuh Panj Copper Deposit. Exploration Department (2007)
Roshani P., Mokhtari A.R., Tabatabaei S.H.: Objective based geochemical anomaly detection—application of discriminant function analysis in anomaly delineation in the Kuh Panj porphyry Cu mineralization (Iran). J. Geochem. Explor. 130, 65–73 (2013)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tabatabaei, S.H., Rodsari, P.R. & Mokhtari, A.R. Predicting Potential Mineralization Using Surface Geochemical Data and Multiple Linear Regression Model in the Kuh Panj Porphyry Cu Mineralization (Iran). Arab J Sci Eng 40, 163–170 (2015). https://doi.org/10.1007/s13369-014-1482-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-014-1482-z