Determination of Cr geochemistry anomaly zones in the Orzooiyeh area, Hormozgan province using Analytical Hierarchy Process (AHP)

زمین شناسی اقتصادی

Article 3, Volume 10, Issue 1 - Serial Number 18, 2018, Pages 47-59   

Document Type: Research Article

DOI: 10.22067/econg.v10i1.48872

Authors

Mehrdad Barati^{* 1}; Akram Ostadhosseini¹; Iraj Rasa²; Mohammad Yazdi²

¹Bu-Ali Sina

²Shahid Beheshti

Abstract

Introduction
Using stream sediment geochemical exploration has been considered as a useful approach to explore the good potentials for many years. Problems might come up in the course of implementing resolving which may requires the use of more recent findings or auxiliary methods.
One of the areas which has faced special problems during the conducting geochemical exploration is the Orzooiyeh region on the border of Kerman and Hormozgan provinces. Stream sediment exploration was carried out in the area in the scale of 1: 100,000. This region includes two different geological zones that are the Sanandaj-Sirjan in the northern part and the Zagros in the southern part. During the statistical analysis and method of eliminating the effects of the upstream rock it was determined that most of the element of chromium’s anomalies are in compliance with the Bakhtiari and Aghajari units which are lacking in the economic importance in the chromium ore. Current geochemical exploration methods often extract the anomalies based on classical statistical methods (Yazdi, 2002). In these methods, the range of the anomaly is just determined based on simple numerical calculations and except for grade, any of the other parameters do not have a significant role in determining the anomaly areas. However, procedures such as fuzzy logic, neural system, regression and hierarchical analysis process enable the users to involve more parameters in data processing (Oh and Lee, 2010; Kumar and Hassan, 2013; Carranza, 2008).
For instance, using special algorithms has made parameters such as lithology and tectonic, geophysics and geochemistry effective in processing and determining the anomaly zones, and ranking each of the affective parameters in the anomaly based on their importance, and eventually achieving the maps and valuable anomaly areas possible (Bonham-carter, 1994; Carranza, 2008).
This study was conducted to identify the significant anomalies zones using AHP and GIS techniques.

Materials and methods
AHP (Analytical Hierarchy Process) is the most comprehensive system designed for multi-criteria decision-making. This method was offered firstly by Sati in 1980, and has carried out numerous applications in different sciences until now. This technique also shows the consistency and inconsistency of the decision that is the outstanding benefit of this technique in multi-criteria decision-making and has been proposed for complex and fuzzy problems based on human brain analysis, and consists of three stages: basic, involving the creation of a hierarchy, determining priorities and logical consistency (Macharis et al., 2004).
In AHP, the factors are compared with each other in pairs and the highest weight is given to the layer that makes the maximum impact on determining the goal (Carranza, 2008). So in this research, after the detection of the effective factors in determining the anomaly areas in the study area, the factors have been weighed for prioritizing the criteria in their order of importance and the paired comparison matrix is formed based on the characteristics of the area and comparative studies for criteria and sub-criteria. After the formation of paired comparison matrices using approximate arithmetic average, the relative weight of parameters was calculated. Then, the researcher carried out the various stages of preparing and the extracting data layer deals with each of these factors in the GIS environment and finally the layers were integrated with each other and the entire range of the anomaly was ranked based on appropriate models.

Results
The results of calculating the final weight criteria show that among the study groups, groups Geochemistry .0.45, lithology 0.45 and tectonic 0.1, respectively, are more significant. The ratio of consistency between these groups is 0.02 and is acceptable and the map prepared by the integration of these groups in the GIS environment according to calculated weights shows that a significant proportion of the false anomalies in the region have been eliminated, and the chromium anomalies associated with ophiolites and peridotites of the region show their best. Therefore, this method can be used for providing the mineral prospecting map that the abandoned mines located in the upstream of anomaly areas confirmed the efficiency of this method in the determination of the anomaly areas.

Discussion
In the present study, the hierarchical process analysis method was utilized to eliminate the false anomalies caused by small and non-significant placer deposits related to the detrital formations in the region. The effective factors in determining the anomaly zones were determined and the final map was constructed by integrating groups, lithology, elemental geochemistry and tectonics that is, the anomaly zones map was drawn in the GIS environment. The results show that the region anomalies are related to ophiolite and ultrabasic and a little bit the region detrital. Therefore, a significant percentage of false anomalies associated with the regional detrital of the area was eliminated by this method and the real anomalies showed their best. This discussion indicates the efficacy of the method of AHP and GIS technique, and they can be considered to be effective methods to reduce the impact of Singenetic factors and naturally to eliminate false anomalies.

References
Bonham-Carter, G.F., 1994. Geographic Information Systems for Geoscientists: Mod-eling with GIS. Pergamon Press, Oxford, UK, 398 pp.
Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier, Amsterdam, Netherlands, 368 pp.
Kumar, S. and Hassan, M., 2013. Selection of a Landfill Site for Solid Waste Management: An Application of AHP and Spatial Analyst Tool. Journal of the Indian Society of Remote Sensing, 41(1): 45–56.
Macharis, C., Springael, J., Brucker, K.D. and 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(2): 307–317.
Oh, H.J. and Lee, S., 2010. Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea. Nonrenewable Resource, 19(2): 103–124.
Yazdi, M., 2002. Conventional methods in geochemical exploration. Shahid Beheshti University, Tehran, 180 pp. (in Persian)

Keywords

Cr-geochemical anomalies; Stream sediment; Orzooiyeh; Hormozgan

References

Abedi, M., Torabi, S.A. and 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 Geofisica Teorica ed Applicata, 54(2): 145–164.

Aghanabati, A., 2004. Geology of Iran. Ministry of Industry and Mines. Geological Survey of Iran, Tehran, 582 pp. (in Persian)

Azizan, H. and Nazemzadeh, M., 2006. Geological map of Orzuiyeh, scale 1:100000. Geological Survey of Iran, Tehran.

Bonham-Carter, G.F., 1994. Geographic Information Systems for Geoscientists: Mod-eling with GIS. Pergamon Press, Oxford, UK, 398 pp.

Bowen, W.M., 1990. Subjective judgments and data environment analysis in site selection. Computers, Environment and Urban Systems, 14(2): 133-144.

Carranza, E.J.M., 2008. Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier, Amsterdam, Netherlands, 368 pp.

Chang, N., Parvathinathanb, G. and Breeden, J.B., 2008. Combining GIS with fuzzy multi criteria decision making for landfill siting in a fast-growing urban region. Journal of Environmental Management, 87(1): 139–153.

Chen, H.S., Liu, G.S., Yang, Y.F., Ye, X.F. and Shi, Z., 2010. Comprehensive evaluation of tobacco ecological suitability of Henan province based on GIS. Agricultural Sciences in China, 9(4): 583–592.

Chen, Y., 2001. Implemetingan Hierarchy Process by Fuzzy integral. International Journal of Fuzzy Systems, 3(3): 493–502.

Cimren, E., Catay, B. and Budak, E., 2007. Development of a machine tool selection system using AHP. The International Journal of Advanced Manufacturing Technology, 35(3–4): 363–376.

Dagdeviren, M., 2008. Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing, 19(4): 397–406.

Dey, P.K. and Ramcharan, E.K., 2000. Analytic hierarchy process helps select site for limestone quarry expansion in Barbados. Journal of Environmental Management, 88(4): 1384–1395.

Dyer, R.F. and Forman, E.H., 1991. An analytical approach to marketing decisions. Englewood Cliffs, Prentice Hall, USA, 368 pp.

Hassani Pak, A.A., 2004. Geochemical exploration. Tehran University, Tehran, 615 pp. (in Persian)

Hassani Pak, A.A. and Sharafaddin, M., 2001. Exploration Data Analysis. Tehran University, Tehran, 990 pp. (in Persian)

Jamshidi, J., Ataei, M., Sereshki, F. and Jalali, S.M.E., 2009. The application of AHP approach to selection of optimum underground mining method, case study: Jajarm Bauxite Mine (Iran). Archives of Mining Sciences, 54(1): 103–17.

Jiajin, Y. and Lee, H., 1997. An AHP decision model for facility location selection. Facilities, 15(9): 241–254.

Kazakis, N., Kougias, I. and Patsialis, T., 2015. Assessment of flood hazard areas at a regional scale using an index -based approach and Analytical Hierarchy Process: Application in Rhodope– Evros region, Greece. Science of the Total Environment, 538(1): 555–563.

Komac, M., 2006. A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia. Geomorphology, 74(1): 17–28.

Kumar, S. and Hassan, M., 2013. Selection of a Landfill Site for Solid Waste Management: An Application of AHP and Spatial Analyst Tool. Journal of the Indian Society of Remote Sensing, 41(1): 45–56.

Kun, M., Topaloglu, S. and Malli, T., 2013. Evaluation of Wheel Loaders in Open Pit Marble Quarrying by Using the AHP TOPSIS Approaches. Archives of Mining Sciences, 58(1): 255–267.

Lopez, H.J. and Zink, J.A., 1991. GIS-assisted modelling of soil-induced mass movement hazards: a case study of the upper Coello river basin, Tolima, Colombia. Interdenominational Theological Center, 4: 202–220.

Macharis, C., Springael, J., Brucker, K.D. and 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(2): 307–317.

Najafi, A., Karimpour, M.H. and Ghaderi, M., 2014. Application of fuzzy AHP method to IOCG prospectivity mappingAcase study in Taherabad prospecting area, eastern Iran. International Journal of Applied Earth Observation and Geoinformation, 33(1): 142–154.

Ngai, E.W.T., 2003. Selection of web sites for online advertising using the AHP. Information and Management, 40(4): 233–242.

Oh, H.J. and Lee, S., 2010. Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea. Nonrenewable Resource, 19(2): 103–124.

Pourghasemi, H.R., Moradi, H.R. and Fatemi Aghda, S.M., 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards, 69(1): 749–779.

Rahimdel, M.J. and Ataei, M., 2014. Application of analytical hierarchy process to selection of primary crusher. International Journal of Mining Science and Technology, 24(4): 519–523.

Rahman, R. and Saha, S.K., 2008. Remote sensing, spatial multi criteria evaluation (SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern planning for a flood prone area. Journal of Spatial Science, 53(2): 161–177.

Rigol-Sanchez, J.P., Chica-Olmo, M. and Abarca-Hernandez, F., 2003. Artificial neural networks as a tool for mineral potential mapping with GIS. International Journal of Remote Sensing, 24(5): 1151–1156.

Saaty, T.L., 1980. The Analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York, USA, 281 pp.

Saaty, T.L., 1986. Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7): 841–855.

Shahabi, H., Khezri, S., Ahmad, B.B. and Hashim, M., 2014. Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models. Catena, 115(1): 55–70.

Shen, L., Muduli, K. and Barve, A., 2015. Developing a sustainable development framework in the context of mining industries: AHP approach. Resources Policy, 46(1): 15–26.

Taheri, K., Gutierrez, F., Mohseni, H., Raeis, E. and Taheri, M., 2015. Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: A case study in Hamadan province, Iran. Geomorphology, 234(1): 64–79.

Wu, C. and Chen, S., 2009. Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology, 112(3): 190–204.

Wu, C., Lin, C. and Chen, H., 2007. Optimal selection of location for Taiwanesehospitals to ensure a competitive advantage by using the analytic hierarchyprocess and sensitivity analysis. Building and Environment, 42(3): 1431–1444.

Yazdi, M., 2002. Conventional methods in geochemical exploration. Shahid Beheshti University, Tehran, 180 pp. (in Persian)

Zhang, D., Agterberg, F., Cheng, Q. and Zuo, R., 2013. A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity. Mathematical Geosciences, 46(7): 869–885.