Gilsonite Mineral Potential of Gilan-e Gharb to Qasr-e Shirin using FAHP-FTOPSIS Prediction Model

Document Type : Research - Paper

Authors

1 Associate Professor, School of Surveying and Geospatial Engineering, Dept. of Engineering, University of Tehran, Tehran, Iran

2 Ph.D Student, Dept. of Mining Engineering, University of Tehran, Tehran, Iran

Abstract

Gilan-e Gharb region has been located in the west of Iran and it has special features in terms of geology. Remote sensing data (satellite images), geology, tectonics and mineral data sets of the region were used to prepare an innovative integrated method for the exploration of Gilsonite mineral. Effective criteria and sub-criteria in the exploration of this mineral were identified and evaluated according to the available exploration data. Afterwards, these criteria were weighted using Fuzzy Analytical Hierarchical Process (FAHP). Accordingly, the ideal positive and negative solutions were ranked using a Fuzzy TOPSIS method to prepare a mineral potential mapping (MPM) of Gilsonite in Gilan-e Gharb region. The product of this paper is a potential map used for optimal identification in the early stages of exploration of Gilsonite mineralization that reduces the time, cost, and risk of exploration. Finally, the field’s visit and investigation has been accomplished for the regions with a high potential of Gilsonite mineralization to evaluate the proposed method. The results showed that 82% of the identified points had proper adjustment to the MPM. This high adaptation showed that the proposed method has good performance in Gilsonite exploration.

Keywords

Main Subjects


  1. Marsh, H., Akitt, H. W., Hurley, J. M., Melvin, J., and Warburton, A. P. (1971). “Formation of graphitisable carbons from gilsonite pitch, and polyvinyl chloride -- a mass spectrometric and NMR study”. Journal of Applied Chemistry and Biotechnology, 21: 251-260.
  2. Lawal, K. A. (2014). “Economics of Steam-Assisted Gravity Drainage for the Nigerian Bitumen Deposit”. Journal of Petroleum Science and Engineering, 116 (April): 28-35.
  3. Abedi, M., Torabi, S. A., Norouzi, G. H., and Hamzeh, M. (2015). ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping”. The Journalof Applied Geophysics, 117: 138-140.
  4. Lee, S., Song, K. Y., Kim, Y., and Park, I. (2012). “Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model”. HydrogeologyJournal, 20: 1511-1527.
  5. Geranian, H., Tabatabaei, S. H., Asadi, H. H., and Carranza, E. J. M. (2016). “Application of discriminant analysis and support vector machine in mapping gold potential areas for further drilling in the Sari-Gunay gold deposit, NW Iran”. Natural Resources Research, 25: 145-159.
  6. Feizi, F., Ramezanali, A. K., and Mansouri, E. (2017). “Calcic iron skarn prospectivity mapping based on fuzzy AHP method, a case study in Varan area, Markazi province”. Journal of Geosciences, 21: 123-126.
  7. Carranza, E. J. M. (2008). “Geochemical anomaly and mineral prospectivity mapping in GIS”. Elsevier science, 11: pp. 368.
  8. Abedi, M., Gholami, A., and Norouzi, G. H. (2013). “A stable downward continuation of airborne magnetic data: a case study for mineral prospectivity mapping in Central Iran”. Computers & Geosciences, 52: 269-280.
  9. Ertugrul, I., and Karakasoglu, N. (2007). “Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods”. Expert Systems with Applications, 36(1): 702-715.
  10. Carranza, E. J. M. (2004). “Weights-of-evidence modelling of mineral potential: A case study using small number of prospects, Abra, Philippines”. Natural Resources Research, 13: 173-187.
  11. Carranza, E. J. M. (2010). “Improved wildcat modelling of mineral prospectivity”. Resource Geology, 60: 129-149.
  12. Hosseinali, F., and Alesheikh, A. A. (2008). “Weighting spatial information in GIS for copper mining exploration”. American Journal of Applied Sciences, 5(9): 1187-1198.
  13. Beikkhakhian, Y., Javanmardi, M., Karbasian, M., and Khayambashi, B. (2015). “The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods”. Expert Systems with Applications, 42: 6224-6236.
  14. Gumus, A. T. (2009). “Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology”. Expert Systems with Applications, 36: 4067-4074.
  15. Baykasoglu, A., Kaplanoglu, V., Durmusoglu, Z., and Sahin, C. (2013). “Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection”. Expert Systems with Applications, 40(3): 899-907.
  16. Saeedpoor, M., Vafadarnikjoo, A., Mobin, M., and Rastegari, A. (2015). “A Seroquel model approach integrated with fuzzy AHP and fuzzy TOPSIS methodologies to rank life insurance firms”. Proceedings of the American Society for Engineering Management. International Annual Conference, Indianapolis, Indiana, USA.
  17. Saaty, T. L. (1980). “Analytic hierarchy process”. New York: McGraw Hill.
  18. Hwang, C. L., and Yoon, K. (1981). “Multiple attributes decision making methods and applications”. Berlin, Springer.
  19. Lai, Y. J., Liu, T. Y., and Hwang, C. L. (1994). “TOPSIS for MODM”. European Journal of Operational Research, 76: 486-500.
  20. Kannan, D., Jabbour, A. B. L. D. S., and Jabbour, C. J. C. (2014). “Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company“. European Journal of Operational Research, 233: 432-447.
  21. Asadi, H. H., Sansoleimani, A., Fatehi, M., and Carranza, E. J. M. (2016). “An AHP–TOPSIS predictive model for district-scale mapping of Porphyry Cu–Au potential: A case study from Salafchegan Area (Central Iran)”. Natural Resources Research, 25(4): 417-429.
  22. Bordenave, M., and Hegre, J. (2010). “Current Distribution of Oil and Gas Fields in the Zagros Fold Belt of Iran and Contiguous Offshore as the Result of the Petroleum Systems”. Geological Society, London, Special Publications, 291-353.
  23. Bourdet, D. (2002). “Well Test Analysis: The Use of Advanced Interpretation Models”. Elsevier, New York, NY, USA, pp. 426.
  24. Dagdeviren, M., Yavuz, S., and Kilinc, N. (2009). “Weapon selection using the AHP and TOPSIS methods under fuzzy environment”. Expert Systems with Applications, 36(4): 8143-8151.