Document Type : Original Research Paper

Authors

1 Exploration of Mining Engineering Dep., Mining Faculty, Tehran University, Tehran, Iran

2 GIS Department, Survey Faculty, K.N.Toosi University of Technology, Tehran, Iran

Abstract

Piles of maps from different sources with varying scales and formats and different styles and absence of a proper solution for integrating vast amount of information has resulted in a complexity for preparing mineral potential map. Using GIS not only organizes the information related to mineral exploration but also has the ability to produce and integrate information layers in different models with more precision and speed and supports spatial decision makings. In this article mineral potential map of Now Chun copper prospect has been produced for determination of drilling points. Used layers in this study include rock type, structure, alteration, mineralization indicators, anomaly zone of chargeability and apparent resistivity and metal factor, anomaly of copper and molybdenum and Cu-Mo additive indexes. After information preparation, Factor maps were weighted and integrated in the inference network. Integration use of Fuzzy logic and index overlay operators in inference network can eliminate defects in other models and provide more flexible integration of factor maps. Regarding to produce mineral potential map, mineral potential zones of porphyry copper were located in north-east parts of studied area. Eventually, the degree of correlation between mineral potential map and those operated exploration boreholes have been estimated for two different classes, 63.16 % and 64.52 %. Comparison between the high potential points indicated by our mineral potential maps with those previous drilled boreholes reveals about 26% discorrelation. It means that if such present study had been done before any drilling operation, it could have saved 200,000$ just for drilling expenditure.    

Keywords

 
 
References
Agterberg, F. P. & Bonham-Carter, G. F., 1990- Deriving weights of evidence from geoscience contour maps for prediction of discrete events. Proceedings of the 22nd APCOM Symposium, Berlin, Germany, v.2, p. 381-395.
Agterberg, F. P., 1992- Combining indicator patterns in weights of evidence modeling for resource estimation. Nonrenewable Resources, v.1, p. 39-50.
Almasi, A., 2007- Results of drilling in Now Chun, National Iranian Copper Industries Company, Exploration management, Pars Olang Company.
An, P., Moon, W. M. & Rencz, A., 1991- Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, v. 27, 1-11.
Asadi, H. H. & Hale, M., 1999- A predictive GIS model for mapping potential gold and base metal mineralization in Takab area, Iran, Computer & Geosciences.9 
Asadi, H. H., 2000- The Zarshuran gold deposit model applied in mineral exploration GIS in iran, PhD Thesis. ITC, Netherlands, 190pp.
Boleneus, D. E., Raines, G. L., Causey, J. D., Bookstrom, A. A., Frost, T. P. & Hyndman, P. C., 2001- Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling. USGS Open-File Report 01-501, 52 pp.
Bonham-Carter, G. F., 1994- Geographic information systems for geoscientists: modeling with GIS, Pergamon Press, Ontario, Canada. 
Brown, W. M., Gedeon, T. D., Groves, D. I. & Barnes, R.G., 2000- Artificial neural networks: a new method for mineral prospectivity mapping: Australian Journal of Earth Sciences, v. 47, p. 757-770.
Carranza, E. J. M., & Hale, M., 2001- Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research, v. 10(2), p. 125-136.
Carranza, J., 2002- Geographically-Constrained mineral potential mapping, PhD Thesis, Delft University of Technology, The Netherlands, 480 pp.
Karimi, M., Menhaj, M. B. & Mesgari, M. S., 2008a- Mineral potential mapping of copper minearls using fuzzy logic in GIS invironment, ISPRS 2008, Beijing, China.
Karimi, M., Valadan Zoj, M., Ebadi, H. & Sahebzamani, N., 2008b- Preparing of Mineral potential map of copper using GIS, Accepted in Geoscience Journal
Malczewski,  J., 1999- GIS and multicriteria decision analysis, John Wiley & Sons INC.
Mukhopadhyay, B., Hazra, N., Sengupta, S. R. & Kumar Das, S., 1996- Mineral potential map by a knowledge driven GIS modeling: an example from Singhbhum Copper Belt, Jharkhad,Geological Survey of India.
Porwal, A., 2006- Mineral potential mapping with mathematical geological models. Ph.D. Thesis, University of Utrecht, The Netherlands, 289 pp.
Porwal, A., Carranza, E. J. M. & Hale, M., 2003- Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research,    v. 12(1), p. 1-25.
Wright, D. F. & Bonham-Carter, G. F., 1996- VHMS favorability mapping with GIS-based integration models, Chisel-Andersen Lake area. Geological Survey of Canada, Bulletin, v. 426, p.339-376.
Yugoslavia report, 1972- Report on explorations for copper in Now Chun area. Institute for geological and mining exploration Beograd-Yugoslavia, p.1-39.
Zadeh, L. A., 1965- Fuzzy sets. IEEE Information and Control, v.8, p. 338-353.