Employing support vector machine, statistical and geostatistical methods to design the detailed exploration grid of Khomein-Robat Pb-Zn deposit

Author

Mining Engineering Department, College of Geosciences Engineering, Arak University of Technology, Arak, Iran

Abstract

In current research, primary classification of Khomein-Robat-Arregijeh Pb-Zn deposit has been ‎carried out first, using support vector machine (SVM) method. To achieve this goal, 548 ‎available multivariate data point comprising 337 induced polarization (IP) and resistivity (Rs) ‎geophysical data, 211 rock type and Pb-Zn assay data from outcrops, trenches and test pits, ‎three variables IP, Rs and rock type were considered as predictive variables as well as Pb-Zn ‎assay as target variable. Afterward through designing and training an SVM model, on the basis ‎of three cut off grade 1.5, 2 and 3 percent, the deposit was classified to two classes: high-grade ‎zone or anomaly (above the cut off grade) and low-grade zone or background (lower than cut ‎off grade). Then exploratory boreholes scattered in the region, were drilled in the locations ‎known as high-grade zone as well as anomalies of the geophysical pseudo-sections. In the next, ‎using all exploration information, designing the detailed exploration grid of the deposit was ‎performed through statistical and geostatistical methods. A rectangular grid with dimensions of ‎‎36*35m was obtained by analytical method based on classical statistics. Employing geostatistical ‎method and 3-D variography for Pb-Zn assays of the boreholes using SGEMS software ‎concluded a square grid with dimensions of 55*55m. According to the results of the research, ‎semi-deep exploration activities are suggested at the nodes of the new exploration grid, designed ‎by geostatistical method due to more accuracy of the method.‎

Keywords

References

Abbaszadeh, M., Hezarkhani, A., Soltani, S., 2013. An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Chemie der Erde 73, 545–554.
Abedi, M., Norouzi, G.H., Baharoudi, A., 2012. Support vector machine for multi-classification of mineral prospectivity area. Computers & Geosciences 46, 272–283.
Ahmadi, R., 2005. Designing optimum exploration grid of metallic deposits with two practical case studies. Iran University of Science and Technology (Arak branch), Vice Chancellor for Research, Arak, p. 67 (In Persian).
Ahmadi, R., 2009. Application of statistical patterns to evaluate ore reserves emphasis to Ali-abad, Yazd copper mine, Arak University of Technology, Vice Chancellor for Research, Arak, p. 102 (In Persian).
Ahmadi, R., 2018a. Designing a preliminary exploration grid of drill holes for the promising area of gold in Delijan region. The 36th national and the 3rd international geosciences congress, 25th-27th February, Tehran, Iran.
Ahmadi, R., 2018b. Application of geometric probability to design exploration grid of mineral deposits, case study: porphyry copper index located in the south-west of Kerman. Journal of Analytical and Numerical Methods in Mining Engineering 8 (15), 39-54.
Ahmadi, R., Hossein-nejad, M.R., 2002. Geochemical investigations to supply the exploratory model and analysis of exploration grid of Arak-Emarat lead-zinc deposit. 21st conference on geosciences. Geological survey of Iran, Tehran (In Persian).
Ahmadi, R., Kheirabadi, A., 2004. Designing optimum exploration grid of iron-manganese deposit of Arak-Shamsabad. The 1st Mining engineering conference of Iran, Tarbiat Modares University, Tehran (In Persian).
Bohling, G., 2007. SGeMS tutorial notes.
Cortes, C., Vapnik, V., 1995. Support vector networks. Machine Learning 20, 273–297.
Hassani-Pak, A.A., 1998. Geostatistics. Tehran University Press, p. 314 (In Persian).
Ivanciuc, O., 2007. Applications of support vector machines in chemistry. Reviews in Computational Chemistry 23, 291–400.
Johnson, R.A., Wichern, D.W., 2002. Applied multivariate statistical analysis, 4th Edition. Prentice-Hall, London.
Kalagari, A.A., 2010. Principles of geophysical explorations, Tabriz, p. 485 (In Persian).
Li, H.D., Liang, Y., Xu, Q., 2009. Support vector machines and its applications in chemistry. Chemometrics and Intelligent Laboratory Systems 95, 188–198.
Madani, H., 1997. Principles of prospecting, exploration and evaluation of ore reserves. Khane Farhang, p. 816 (In Persian).
Pichab Kansar consultant engineers Co., 2009. Geological report of Robat exploration region with supplying 1:20000 geology map, p. 427 (In Persian).
Smirnoff, A., Boisvert, E., Paradis, S., 2008. Support vector machine for 3D modelling from sparse geological information of various origins,. Computers & Geosciences 34, 127–143.
Twarakavi, N., Misra, D., Bandopadhyay, S., 2006. Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research 15(1), 15-26.
Yu, H., Kim, S., 2012. SVM tutorial: classification, regression, and ranking, Handbook of Natural Computing, Springer Berlin Heidelberg, 479-506.
Yu, L., Porwal, A., Holden, E., Dentith, M., 2012. Towards automatic lithological classification from remote sensing data using support vector machines. Computers & Geosciences 45, 229-239.
Zuo, R., Carranza, E.J.M., 2011. Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences 37, 1967–1975.
Advanced Applied Geology
Volume 10, Issue 2
July 2020
Pages 121-136

Files

Share

How to cite

Statistics