A COMPARATIVE ANALYSIS OF INDEX OVERLAY AND TOPSIS (BASED ON AHP WEIGHT) FOR IRON SKARN MINERAL PROSPECTIVITY MAPPING, A CASE STUDY IN SARVIAN AREA, MARKAZI PROVINCE, IRAN

Yıl 2017, Cilt: 155 Sayı: 155, 147 - 160, 03.12.2017

Edris Mansouri Faranak Feizi Alireza Jafari Rad Mehran Arian

https://doi.org/10.19111/bulletinofmre.305201

Cited By: 3

Öz

The aim of this research is to compare index overlay and TOPSIS (based on AHP weight) for predictive
Skarn potential map. In this paper, for Calcic Iron Skarn mineralization, criteria and subcriteria introduced
and ranked for generating mineral prospectivity map. The values of fi nal prospecting maps for Skarn
deposit by index overlay and TOPSIS methods was specifi ed by dividing the prospectivity values into
10 classes. For better comparison, values assign to classes base on their priority in mineral exploration.
The comparative analyses of index overlay and TOPSIS integration methods, has been performed by
selecting four GCPs for fi eld checking. Field observation in GCP 1, 2 and 3, confi rmed Iron mineralization
in the contact of intrusive bodies with sedimentary units, where the contact metamorphism was obvious
but there is no observable mineralization in GCP4. Although high magnetic is distinct in mentioned
GCP. Based on the fi eld checking in Sarvian area, the TOPSIS method has more accuracy compared
to index overlay approach. Therefore, the TOPSIS method recommends for Calcic Iron Skarn Mineral
Prospectivity Mapping in Sarvian and adjacent area.

Anahtar Kelimeler

TOPSIS, Index Overlay, AHP, Iron Skarn, Iran

A COMPARATIVE ANALYSIS OF INDEX OVERLAY AND TOPSIS (BASED ON AHP WEIGHT) FOR IRON SKARN MINERAL PROSPECTIVITY MAPPING, A CASE STUDY IN SARVIAN AREA, MARKAZI PROVINCE, IRAN

Öz

The aim of this research is to compare index overlay and TOPSIS (based on AHP weight) for predictive Skarn potential map. In this paper, for Calcic Iron Skarn mineralization, criteria and subcriteria introduced and ranked for generating mineral prospectivity map. The values of fi nal prospecting maps for Skarn deposit by index overlay and TOPSIS methods was specifi ed by dividing the prospectivity values into 10 classes. For better comparison, values assign to classes base on their priority in mineral exploration. The comparative analyses of index overlay and TOPSIS integration methods, has been performed by selecting four GCPs for fi eld checking. Field observation in GCP 1, 2 and 3, confi rmed Iron mineralization in the contact of intrusive bodies with sedimentary units, where the contact metamorphism was obvious but there is no observable mineralization in GCP4. Although high magnetic is distinct in mentioned GCP. Based on the fi eld checking in Sarvian area, the TOPSIS method has more accuracy compared to index overlay approach. Therefore, the TOPSIS method recommends for Calcic Iron Skarn Mineral Prospectivity Mapping in Sarvian and adjacent area.

Anahtar Kelimeler

TOPSIS, Index Overlay, AHP

Kaynakça

• Abedi, M., Norouzi, G. H., 2012. Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics, 83, 35-45.
• Abedi, M., Norouzi, G. H., Bahroudi, A. 2012a. Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences, 46, 272-283.
• Abedi, M., Torabi, S. A., Norouzi, G. H., Hamzeh, M., Elyasi, G. R. 2012b. PROMETHEE II: a knowledge-driven method for copper exploration. Computers & Geosciences, 46, 255-263.
• Abedi, M., Torabi, S. A., Norouzi, G. H., & Hamzeh, M. 2012c. ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of applied geophysics, 87, 9-18.
• Abedi, M., Torabi, S. A., Norouzi, G. H. 2013. Application of fuzzy AHP method to integrate geophysical data in a prospect scale, a case study: Seridune copper deposit. Bollettino di GeofisicaTeoricaedApplicata, 54, 145-164.
• Bonham-Carter, G.F. 1994. Geographic Information Systems for geoscientists-modeling with GIS. Pergamon Press, Oxford, UK, 398.
• Carranza, E. J. M. 2008. Geochemical anomaly and mineral prospectivity mapping in GIS, Handbook of Exploration Environmental Geochemistry. Elsevier, Amsterdam, Netherlands, 368.
• Carranza, E.J.M., and Hale, M. 2001. Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research, 10, 125–136.
• Dağdeviren, M. 2008. Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing, 19, 397-406.
• Dağdeviren, M., Yavuz, S., Kılınç, N. 2009. Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36, 8143-8151.
• Feizi, F., Mansouri, E. 2012. Identification of Alteration Zones with Using ASTER Data in A Part of Qom Province, Central Iran, Journal of Basic and Applied Scientific Research, 2,73-84.
• Feizi, F., Mansouri, E. 2013a. Separation of Alteration Zones on ASTER Data and Integration with Drainage Geochemical Maps in Soltanieh, Northern Iran, Open Journal of Geology, 3, 134-142.
• Feizi, F., Mansouri, E. 2013b. Introducing the Iron Potential Zones Using Remote Sensing Studies in South of Qom Province, Iran. Open Journal of Geology, 3, 278-286.
• Hassan-Nezhad, A.A., Moore, F. 2006. A stable isotope and fluid inclusion study of the Qaleh-Zari Cu–Au–Ag deposit, Khorasan Province, Iran. Journal of Asian Earth Sciences. 27, 805–818.
• Hwang, C.L., Yoon, K. 1981. Multiple Attribute Decision Making-Methods and Applications: A State of the Art Survey. Springer, New York.
• Macharis, C., Springael, J., De Brucker, K., Verbeke, A. 2004. PROMETHEE and AHP: The design of operational synergies in multicriteria analysis: Strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research, 153, 307-317.
• Malczewski, J. 2006. Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis, International Journal of Applied Earth Observation and Geo information, 8, 270-277.
• Mansouri, E., Feizi, F., KarbalaeiRamezanali, A. A. 2015. Identification of magnetic anomalies based on ground magnetic data analysis using multifractal modelling: A case study in Qoja-Kandi, East Azerbaijan Province, Iran. Nonlinear Processes in Geophysics, 22, 579-587.
• Najafi, A., Karimpour, M. H., Ghaderi, M. 2014. Application of fuzzy AHP method to IOCG prospectivity mapping: A case study in Taherabad prospecting area, eastern Iran. International Journal of Applied Earth Observation and Geoinformation, 33, 142-154.
• Noori, R., Feizi, F., Jafari, M. R. 2011. Determination of Cu and Mo Potential Targets in the Khatunabad Based on Analytical Hierarchy Process, West of Mianeh, Iran. World Academy of Science, Engineering and Technology, 78, 828-831.
• Nouri, R., Afzal, P., Arian, M., Jafari, M., Feizi, F. 2013. Reconnaissance of Copper and Gold Mineralization Using Analytical Hierarchy Process (AHP) in the Rudbar 1: 100,000 Map Sheet, Northwest Iran. Journal of Mining and Metallurgy A: Mining, 49, 9-19.
• Nouri, R., Jafari, M. R., Arain, M., Feizi, F. 2012. Hydrothermal Alteration Zones Identification Based on Remote Sensing Data in the Mahin Area, West of Qazvin Province, Iran. In Proceedings of World Academy of Science, Engineering and Technology 67, World Academy of Science, Engineering and Technology.
• Önüt, S., Soner, S. 2008. Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management, 28, 1552-1559.
• Pazand, K., Hezarkhani, A., Ataei, M. 2012. Using TOPSIS approaches for predictive porphyry Cu potential mapping: A case study in Ahar-Arasbaran area (NW, Iran). Computers & Geosciences, 49, 62-71.
• Pazand, K., Hezarkhani, A., Ataei, M., Ghanbari, Y. 2011. Combining AHP with GIS for predictive Cu porphyry potential mapping: a case study in Ahar Area (NW, Iran). Natural resources research, 20, 251-262.
• Pirmoradi, A., Vaezi, H., Baktash, P., Amiri, A. 2012. Role of SDI in index overlay Modeling and fuzzy logic in GIS to predict Malaria outbreak. Canada, Proceedings of Global Geospatial Conference 2012 Québec City, 14-17.
• Porwal, A., Carranza, E.J.M., and Hale, M. 2003. Artificial neural networks for mineral–potential mapping: a case study from Aravalli Province, Western India. Natural resources research, 12, 156–171.
• Porwal, A., Carranza, E.J.M., Hale, M. 2004. A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology, 36, 803-826.
• Porwal, A., Carranza, E.J.M, and Hale, M. 2006. A hybrid fuzzy weights–of–evidence model for mineral potential mapping. Natural Resources Research, 15, 1–14.
• Purtov, V.K., Kholodnov, V.V., Anfilogov, V.N., Nechkin, G.S. 1989. The role of chlorine in the formation of magnetite skarns: International Geology Review. 31, 63-71.
• Rogge, D.M., Halden, N.M., and Beaumont-Smith, C. 2006. Application of data integration for shear–hosted Au potential modelling: Lynn Lake greenstone belt, northwestern Manitoba, Canada. GIS for the Earth Sciences, 44, 191–210.
• Saaty, T.L. 1977. A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15, 234-281.
• Saaty, T.L. 1980. The analytic hierarchy process: planning, priority setting, resources allocation. New York: McGraw, pp, 281.
• Saaty, T.L., 2005, The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making." Multiple criteria decision analysis: state of the art surveys. Springer New York, 345–408.
• Sahandi, M.R., Delavar, S.T., Sadeghi, M., Jafari, A., Moosavi, A. 2005. Geological Map of Iran (1:1,000,000). Geological Survey of Iran. <http://www.ngdir.ir>.
• Sokolov, G.A., Grigorev, V.M. 1977. Deposits of iron, in smirnov, V.I., ed., Ore deposits of the USSR: Pittman, London, 1, 7-13.
• Vidal, C., Injoque-Espinoza, J., Sidder, G.B., and Mukasa, S.B. 1990. Amphibolitic Cu-Fe skarn deposits in the central coast of Peru. Economic Geology, 85, 1447-1461.
• Yousefi, M., Carranza, E. J. M. 2015. Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69-81.
• Yousefi, M., and KamkarRouhani, A. 2010. Principle of Mineral Potential Modeling Techniques (In Geographical Information System): Amirkabir University of Technology Press, Tehran.