JGIS  Vol.7 No.6 , December 2015
Lead and Copper Mineral Prospectivity Mapping in Kalatereshm Area, Based on Exploratory Data Sets Using AHP-Index Overlay Modeling in GIS (Semnan Province, North of Iran)
ABSTRACT
The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zinc. The Synthesis process is done by the Analysis Hierarchy Process (AHP) and Index Overlay (IO) methods. Of previous studies on the area, various companies providing Geological maps and in particular the company of Jiangxi providing its own geochemical maps can be mentioned. The reasons for doing this research and its innovation in Kalatereshm’s sheet can be justified as to be valuable and the fact that we would be able to save in time and cost by doing so. Previous case studies on this particular region lacked the necessary use of an advanced software and method. The informational layers included geochemical layers (the second and first ratings were given to Copper and Lead respectively by weighting based on AHP method), geology layer (the fourth and second ratings were given to Copper and Lead respectively by weighing based on AHP method), fault layer (the first and fourth ratings were given to Copper and Lead respectively by weighting based on AHP method), satellite imagery layer (the third rating was given to both Copper and Lead by weighting based on AHP method) and the more applicable areas for field exploration and detailed procedures of exploration had been determined (the mentioned ratings were delineated by each element’s respective weight in each layer and their importance in the Synthesis of informational layers).

Cite this paper
Ahmadi, S. , Jafarirad, A. and Nezafati, N. (2015) Lead and Copper Mineral Prospectivity Mapping in Kalatereshm Area, Based on Exploratory Data Sets Using AHP-Index Overlay Modeling in GIS (Semnan Province, North of Iran). Journal of Geographic Information System, 7, 677-691. doi: 10.4236/jgis.2015.76055.
References
[1]   Porwal, A.K. andKreuzer, O.P. (2010) Mineral Prospectivity Analysis and Quantitative Resource Estimation. Ore Geology Reviews, 38, 121-127. http://dx.doi.org/10.1016/j.oregeorev.2010.06.002

[2]   Eschmitt, E. (2010) Weights of Mineral Evidence Prospectivity Modeling with ArcGIS.
http://www.mdru.ubc.ca/home/courses/SC62_GIS/ESchmitt_EOSC448_DirStudies.pdf

[3]   Torres, C.A. (2007) Mineral Exploration Using GIS and Processed Aster Images, Advanced GIS EES 6513, University of Texas at San Anthonio, San Antonio.
http://www.utsa.edu/lrsg/Teaching/EES6513/Projects/Mineral%20Exploration%20using%20GIS%20Final.pdf

[4]   Karimi, M. (2003) Design and Implementation for Geographic Information System for Copper Mine Exploration in Detailed Stage. Msc Thesis, K. N. Toosi University of Technology, Iran, 120 p.

[5]   Harris, J., Wilkinson, L. and Grunsky, E. (2000) Effective Use and Interpretation of Lithogeochemical Data in Regional Mineral Exploration Programs. https://www.researchgate.net/publication/223606604_ Effective_use_and_interpretation_of_lithogeochemical_data_in_regional_mineral_exploration_programs_ Application_of_Geographic_Information_Systems_GIS_technology

[6]   Rowe, G. and Wright, G. (2001) Expert Opinions in Forecasting: The Role of the Delphi Technique.
http://link.springer.com/chapter/10.1007%2F978-0-306-47630-3_7

[7]   Zhou, W., Chen, G., Li, H., Luo, H. and Huang, S. (2007) GIS Application in Mineral Resource Analysis—A Case Study of Offshore Marine Placer Gold at Nome, Alaska. Computers and Geosciences, 33, 773-788.
http://dx.doi.org/10.1016/j.cageo.2006.11.001

[8]   Wilkinson, J.J. (2000) Fluid Inclusions in Hydrothermal Ore Deposits.
http://www3.imperial.ac.uk/pls/portallive/docs/1/31629696.PDF

[9]   Beane, R.E. and Bodnar, R.J. (1995) Hydrothermal Fluids and Hydrothermal Alteration in Porphyry Copper Deposits.
https://www.researchgate.net/file.PostFileLoader.html?id=552e131cd3df3e152e8b45b4&assetKey=AS%3A273757380251648%401442280253901

[10]   Bradly, D.C. and Leach, D.L. (2002) Tectonic Controls of Mississippi Valley-Type Lead-Zinc Mineralization in Orogenic Forelands. http://alaska.usgs.gov/staff/geology/bradley/bradley_leach_12202.pdf

[11]   Partington, G.A. and Sale, M.J. (2004) Prospictivity Mapping Using GIS with Publicly Available Earth Science Data—A New Targeting Tool Being Successfully Used for Exploration in New Zealand.
http://kenex.com.au/documents/papers/PacRimPartington_Sale.pdf

[12]   Yager, D.B., Hofstra, A.H. and Granitto, M. (2012) Analyzing Legacy US Geological Survey Geochemical Databases Using GIS—Applications for a National Mineral Resource Assessment.
http://pubs.usgs.gov/tm/11c05/contents/TM11-C5.pdf

[13]   Saaty, T.L. (1980) The Analytical Hierarchy Process, Planning, Priority, Resource Allocation. McGraw-Hill, New York.

[14]   Saaty, T.L. (1980) Decision Making with the Analytic Hierarchy Process.
http://www.colorado.edu/geography/leyk/geog_5113/readings/saaty_2008.pdf

[15]   Aczel, J. and Saati, T. (1983) Procedure for Synthesizing Ratio Judgments. Journal of Mathematical, 27, 93-102. http://www.sciencedirect.com/science/article/pii/0022249683900287

[16]   Yang, X., Skidmore, A.K., Melick, A.R., Zhou, Z. and Xu, J. (2006) Mapping Non-Wood Forest Product Using Logistic Regression and a GIS Expert System. Ecological Modelling, 198, 208-218.
http://dx.doi.org/10.1016/j.ecolmodel.2006.04.011

[17]   Bohman-Carter, G.F. (1994) Geographic Information Systems for Weights-of-Evidence Modeling. Natural Resources Research. Pergamon Press, Oxford, 398.

 
 
Top