Application of fuzzy modeling to identify the hydrothermal alterations in the Ramand region of Qazvin

Document Type : Research Article

Authors

1 Imam Khomeini International University

2 Payam Noor

Abstract

Introduction
Complete recognition of exploration criteria is considered as an important step in the systematic and scientific analysis of mineral potential mapping. The starting point of this analysis is recognition of the mineral potentials and the mechanism of their formation in different geological areas. In order to achieve such goals we can benefit from the known mineralization information. Integrating and analyzing all the data in the geographic information systems (GIS) can lead to the identification of promising mineral areas for future studies (San Soleimani et al., 2011). GIS can be used to organize, process, analyze and integrate the results of geological, geochemical and geophysical, tectonic and alteration studies in order to identify and evaluate the potential of different minerals (Bonham Carter, 1998). The aim of this paper is to apply the fuzzy integration method in GIS with using all the information, without the need to simplify them, in less time and with acceptable accuracy in order to recognize mineral potentials in the Ramand region. Investigation of mineral potentials in this area with using fuzzy logic in addition to the scientific-research aspects, can provide the discovery of appropriate patterns for other mineral resources in Iran.

Materials and method
The first step in any exploration project is that mapping is done with the aim of optimal potential to provide a model for the exploration of possible deposits in the region. This process in next steps will be continued with an extraction of useful information from various data that are contained in the database and this done work is done based on the properties of the elements under prospecting and the relevant exploration models. However, in the final stage, the combination of these maps is based on different models (Bonham Carter, 1998). The pre-requisite for the production of the mineral potential map, is to determine the weight and value which reflects the relative importance of the data classification (Hosseinali and Alesheikh, 2008). According to the study's default that is modeling with using fuzzy logic, a range of values between zero and one can be used to express the degree or the value of a collection (Novriadi and Darijanto, 2006) This is such that zero means lack of full membership and value of one is meant to be a full member of the collection. So, other set members can also be allocated values between zero and one and based on the degree of their membership to the set. So, it can be said that in the mining exploration, membership values are used to indicate the relative importance of each map as well as the relative importance of each class of a single map (Bonham Carter, 1998). Exploratory layers produced in GIS (in the fuzzy theory section), appear in the role of fuzzy sets and are combined by fuzzy operators (Table 1). The combining layers in the fuzzy method express the concept from the optimal options.

Discussion
In this study, some of the geo-referenced data consisting of geological data, remote sensing, tectonic and geophysical data have been used. Geological data for separate lithological units in the region and the identification of faults, remote sensing data to identify hydrothermal alterations and fault lineaments and airborne geophysical data in order to detect magnetic fault lineaments and check the changes of magnetic susceptibility related to hydrothermal alterations are used so that after collecting and entering data, processing the necessary studies has been carried out on them. Finally, by combining the results of each layer and giving the fuzzy weighting to them based on their importance in mineralization and the use of the fuzzy algorithm and gamma (the fuzzy operator) in GIS, the final map is obtained to identify the potential areas with using it in the Ramand region. The possibility to acquire the exploration pattern of base and precious metals is provided in the mineral-prone areas.
Results
In this study, there was a lot of exploration of information such that the decision to determine the potential points and continuing the exploration activities had been made very difficult. Thus, with use of the fuzzy integration method in GIS, all of the information were managed without any need to simplify them, in less time and with acceptable accuracy. Fuzzy logic is a method based on expert knowledge that is used for integration of exploration data and producing the optimal potential map of the Ramand regional reserves. It
points to promising and priority areas for accurate and detailed information. Therefore, it is better to carry out exploration operations and reduces the cost and time as well as expedition to decision-making. And accuracy is very effective.

References
Bonham Carter, G.F., 1998. Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, Oxford, 398 pp.
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.
Novriadi, H.P.M. and Darijanto, T., 2006. Applying Fuzzy Logic Method in Mineral Potential Mapping for Epithermal Gold Mineralization in the Island of Flores, East Nosa Tenggara Using Geographical Information Systems (GIS). Proceeding of 9th International Symposium on Mineral Exploration, Institut Teknologi Bandung, Bandung, Indonesia.
San soleimani, A., Asadi Haroni, H., Tabatabaei, S.H. and Samari, H., 2011. Mineral potential mapping at 1:10000 scale geological map of Kahak using Fuzzy Logic method. 5th National Conference of Geology and Environment, Islamic Azad University, Eslamshahr, Iran. (in Persian with English abstract)

Keywords


Abbasi, S. and Yassaghi, A., 2011. Using landsat images and magnetic field data in identifying fault lineaments and analysis of their origin in the Lorestan Province, Zagros Folded Belt. Iranian Journal of Remote Sensing and GIS, 3(1):19–33. (in Persian)
An, P., Moon, W.M. and Rencz, A., 1991. Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, 27(1): 1–11.
Bonham Carter, G.F., 1998. Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, Oxford, 398 pp.
Crosta, A.P. and Moore, J., 1989. Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minais Gerais State, Brazil; A Prospecting Case History in Greenstone Belt Terrain. Proceedings of the 7th International Conference Applied Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, USA.
Eghlimi, B., Mosavvari, F. and Mehrpartou, M., 1999. Geological map of Danesfehan (Khyarj), scale 1:100,000. Geological Survey of Iran. (in Persian)
Ezzati, A., Mehrnia, R. and Ajayebi, K. 2014. Detection of Hydrothermal Potential Zones Using Remote Sensing Satellite Data in Ramand Region, Qazvin Province, Iran. Journal of Tethys, 2(2): 93–100.
Fadavi, P. and Mehrnia, S.R., 2015. The Use of Airborne Gravity and Magnetic Data bases for Recognizing of Hidden Seismogenic Faulted Systems in South of Tehran (Ivanky Region). 33rd National Geosciences Symposium, Geological Survey of Iran, Tehran, Iran. (in Persian with English abstract)
Harris, J.R., Wilkinson, L., Heather, K., Fumerton, S., Bernier, M.A., Ager, J. and Dahn, R., 2001. Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps- A Case Study: Mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada. Natural Resources Research, 10(2):3–13.
Hassani Pak, A.A., 2010. Principles of Geochemical Exploration. University of Tehran Press, Tehran, 615 pp. (in Persian)
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.
Karimi, M., Menhaj, M.B. and Mesgari, M.S., 2008. Preparing mineral potential map using fuzzy logic in GIS environment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (B8): 1263–1270.
Karimpour, M.H., Heidarian shahri, M.R. and Malekzadeh shafaroudi, A., 2012. Ore deposit exploration. Ferdowsi University of Mashhad, Mashhad, 632 pp.‬
Masoudi, F., 1989. Stratigraphy, petrography and petrology of volcanic rocks at south of Buin-Zahra. M.Sc. Thesis, University of Tarbiat Moallem, Tehran, Iran, 207pp. (in Persian with English abstract)
Mehrnia, S.R., 2015. Introducing the band ratio of M-ratio and its application in the diagnosis of hydrothermal alterations (case study: Ramand region in the southern of Qazvin province). 19th Congress of Iran Geology and 9th Geology National Conference, Payam Noor University of Tehran, Tehran, Iran. (in Persian)
Mukhopadhyay, B., Hazra, N., Kumar Das, S. and Sengupta, S.R., 2002. Mineral potential map by a knowledge driven GIS modeling: an example from Singhbhum Copper Belt, Jharkhad. Proceedings of 5th annual international conference Map India, Geological Survey of India, New Delhi, India.
Norouzi, G.H. and Mehrnia, S.R. 1998. Magnetic Data Application for Sar-eyn Geothermal Exploration (Ardabil province). Journal of Faculty of Engineering, University of Tehran, 31(1):71–81. (in Persian)
Novriadi, H.P.M. and Darijanto, T., 2006. Applying Fuzzy Logic Method in Mineral Potential Mapping for Epithermal Gold Mineralization in the Island of Flores, East Nosa Tenggara Using Geographical Information Systems (GIS). Proceeding of 9th International Symposium on Mineral Exploration, Institut Teknologi Bandung, Bandung, Indonesia.
Porwal, A., Carranza, E.J.M. and Hale, M., 2003. Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12(1): 1–25.
Sabins, F.F., 2002. Remote sensing principle and interpretation. William H. Freeman & Company, New York, 494 pp.
San soleimani, A., Asadi Haroni, H., Tabatabaei, S.H. and Samari, H., 2011. Mineral potential mapping at 1:10000 scale geological map of Kahak using Fuzzy Logic method. 5th National Conference of Geology and Environment, Islamic Azad University, Eslamshahr, Iran. (in Persian with English abstract)
Telford, W.M., Geldart, L.P., Sheriff, R.E. and Keys, D.A. (translated by Zomorrodian, H. and Hajeb-Hosseinieh, H.), 2009. Applied Geophysics. Vol.1, University of Tehran Press, Tehran, 689 pp. (in Persian)
Whitney, D.L. and Evans, B.W., 2010. Abbreviations for names of rock-forming minerals. American Mineralogist, 95(1): 185–187.
Wright, D.F. and Bonham Carter, G.F., 1996. VHMS favorability mapping with GIS-based integration models, Chisel-Andersen Lake area. Geological Survey of Canada, Bulletin, 426(1): 339–376.
Ziaii, M., Pouyan, A. and Ziaei, M., 2009. A Computational Optimized Extended Model for Mineral Potential Mapping Based on Wofe Method. American Journal of Applied Sciences, 6(2): 200–203.
CAPTCHA Image