تعیین دگرسانی‌های هیدروترمال مس پورفیری با استفاده از روشهای آنالیز چند متغیره بر روی داده‌های لیتوژئوشیمیایی در منطقه حراران، استان کرمان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه یزد

2 دانشگاه شهید باهنر کرمان

چکیده

روش­های آماری چند متغیره کاربردهای زیادی در علوم وابسته به زمین شناسی خصوصا ژئوشیمی پیدا کرده­اند. در این مطالعه از آنها به عنوان کاربردی جدید برای تشخیص دگرسانی­های نوع آرژیلیک، پتاسیک و پروپلیتیک (دگرسانی­های عمده موجود در کانسار­های مس پرفیری) استفاده شده است. منطقه حراران واقع شده است. برای تعیین مناطق دگرسان شده از 607 نمونه لیتوژئوشیمیایی استفاده شده است؛ این نمونه­ها از منطقه حراران واقع در در نقشه 1:100000 شهرستان بافت استان کرمان  اخذ شده­اند که در این منطقه کانی­زایی مس پورفیری رخ داده است. نمونه­ها برای 45 عنصر به روش ICP-MS و در آزمایشگاه  Amdelاسترالیا آنالیز شده اند که از بین آنها عناصر Ca, Na, Al, Fe, S, K, Rb, Mgبرای انجام این پژوهش انتخاب شدند.  نتایج آنالیز خوشه­ای بر روی متغیر­ها و بر روی نمونه­ها دو خوشه نشان دادند: خوشه اول شامل عناصر K, Rb, S, که منطبق بر دگرسانی نوع پتاسیک و آرژیلیک و خوشه دوم نیز شامل عناصر Mg, Ca, Na, Al, Fe است که دگرسانی نوع پروپلیتیک را نشان می­دهند. نتایج آنالیز فاکتوری نیز نتایج فوق را تایید کردند و حتی نقشه فاکتور اول در قسمت­های جنوبی منطقه، نواحی کوچکی از آلتراسیون آرژیلیک و پتاسیک را نیز مشخص کرد که این مناطق توسط روش آنالیز خوشه­ای بر روی نمونه­ها مشخص نشده بودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Determination of Cu porphyry hydrothermal alterations using multivariate analysis methods on lithogeochemical data in Hararan area, Kerman province

نویسندگان [English]

  • somaie abbaszadeh 1
  • Abdolhamid Ansari 1
  • Gholam Reza Rahimi Pour 2
چکیده [English]

Summary
In geological sciences, especially geochemistry, multivariate statistical methods have many applications. So that, they were employed as a new application for recognition main alterations in Cu-Mo porphyry system such as argillic, potassic, and propylitic alterations. For determination of alteration areas, results of 607 lithogeochemical samples were utilized in Hararan area, which appears to possess the potentiality for porphyry mineralization and locates in Baft sheet (1:100,000 series) in the southeast of Iran. 607 rock samples were analyzed by ICP-MS in Amdel laboratory for 45 elements. However, it used just the results of Ca, Na, Al, Fe, S, K, Rb, Mg elements for this paper.  Results of hierarchical cluster and k- means Cluster methods showed two different clusters: K, Rb, S elements and Mg, Ca, Na, Al, Fe elements have been in cluster 1 and cluster 2, respectively. Cluster1 and cluster2 introduced as potassic and argillic alterations and propylitic alteration, respectively. The results of factor analysis not only confirmed the above results but also the first factor map depicted small areas of argillic and potassic alterations in the southern parts of the region, so that these areas did not identify by k-means cluster method.
 
Introduction
Hydrothermal Alteration plays a key role in types of mineralization. For recognition of alteration zones, there are common techniques such as mineralogical, lithological studies and field surveys. Researchers used different methods for identifying alteration zones. Therefore, in this study, it was utilized multivariate analysis techniques as a new application for mentioned objective.
 
Methodology and Approaches
In regional geochemistry an advantage would be that instead of presenting maps for 40–50 (or more) elements only maps of 4–6 factors may have to be presented, containing a high percentage of the information of the single element maps. It is even more informative if factor analysis can be used to reveal unrecognized multivariate structures in the data that may be indicative of certain geochemical processes, or, in exploration geochemistry, of hidden mineral deposits. Cluster analysis is used to delineate the relationships between elements and shows which elements tend to have similar variations in concentrations.
 
Results and Conclusions
In this study, 607 lithogeochemical samples were analyzed for 44 elements. But Ca, Na, Al, Fe, S, K, Rb, Mg were selected to recognize alteration zones in Hararan area. Before performing multivariate analysis, the preprocessing of geochemical data was performed including the replacement of censored and outlier data. The results of factor and cluster analysis showed that Fe, Ca, Al, Na, Mg elements and K, Rb, S elements represented Propylitic and argilic alteration, respectively.

کلیدواژه‌ها [English]

  • Potassic alteration
  • Argillic alteration
  • propylitic alteration
  • multivariate analysis
[1]           Khakzad, A.Jafari, H. R. (2003). Mineralogy, paragenesis and economic geology of copper deposits in the Hararan area, Kerman province. Proc. 10th Symposium of Crystallography and Mineralogy of Iran. (In Persian).
[2]           Akbarpour, A.,Gholami, N.,Azizi, H.Torab, F. M. (2012). Cluster and R-mode factor analyses on soil geochemical data of Masjed-Daghi exploration area, northwestern Iran. Arabian Journal of Geosciences, 6 (9), 3397-3408.
[3]           Hassanipak, A. A.  (2005).Designing of exploration projects (geochemical, geophysical and drilling).Tehran University Press. (In Persian)
[4]           Mokhtari, A. R. (2014). Hydrothermal alteration mapping through multivariate logistic regression analysis of lithogeochemical data. Journal of Geochemical Exploration, 145 (0), 207-212.
[5]           Soltani, F.,Afzal, P.Asghari, O. (2014). Delineation of alteration zones based on Sequential Gaussian Simulation and concentration–volume fractal modeling in the hypogene zone of Sungun copper deposit, NW Iran. Journal of Geochemical Exploration, 140 (0), 64-76.
[6]           Praveena, S.,Ahmed, A.,Radojevic, M.,Abdullah, M. H.Aris, A. (2007). Factor-cluster analysis and enrichment study of mangrove sediments-an example from Mengkabong, Sabah. Malaysian J.Anal.Sci, 11 (2), 421-430.
[7]           Roy, A. (1981). Application of cluster analysis in the interpretation of geochemical data from the Sargipalli lead-zinc mine area, Sundergarh district, Orissa (India). Journal of Geochemical Exploration, 14245-264.
[8]           El-Makky, A. M. (2011). Statistical Analyses of La, Ce, Nd, Y, Nb, Ti, P, and Zr in Bedrocks and Their Significance in Geochemical Exploration at the Um Garayat Gold Mine Area, Eastern Desert, Egypt. Natural resources research, 20 (3), 157-176.
[9]           Nude, P. M. (2012). Identifying Pathfinder Elements for Gold in Multi-Element Soil Geochemical Data from the Wa-Lawra Belt, Northwest Ghana: A Multivariate Statistical Approach. International Journal of Geosciences, 03 (01), 62-70.
[10]         Embui, V. F.,Omang, B. O.,Che, V. B.,Nforba, M. T.Suh, E. C. (2013). Gold grade variation and stream sediment geochemistry of the Vaimba-Lidi drainage system, northern Cameroon (West Africa). Natural Science, 5282.
[11]         Yaylalı-Abanuz, G.,Tüysüz, N.Akaryalı, E. (2012). Soil geochemical prospection for gold deposit in the Arzular area (NE Turkey). Journal of Geochemical Exploration, 112 (0), 107-117.
[12]         Darwish, M. a. G.Poellmann, H. (2010). Geochemical exploration for gold in the Nile Valley Block (A) area, Wadi Allaqi, South Egypt. Chemie der Erde-Geochemistry, 70 (4), 353-362.
[13]         Honarpazhouh, J.,Hassanipak, A. A.Seifpanahi Shabani, K. (2013). Integration of Stream Sediment Geochemical and Aster Data for Porphyry Copper Deposit Exploration in Khatun Abad, North West of Iran/Integracja geochemicznych danych o osadach dennych oraz danych pozyskanych z systemu aster do poszukiwań geologicznych w rejonie złóż miedzi porfirytowej w khatun abad, w północno-zachodniej części iranu. Archives of Mining Sciences, 58 (1), 37-54.
[14]         Khorasanipour, M.,Tangestani, M.Naseh, R. (2012). Application of multivariate statistical methods to indicate the origin and geochemical behavior of potentially hazardous elements in sediment around the Sarcheshmeh copper mine, SE Iran. Environmental Earth Sciences, 66 (2), 589-605.
[15]         Idris, A. M. (2008). Combining multivariate analysis and geochemical approaches for assessing heavy metal level in sediments from Sudanese harbors along the Red Sea coast. Microchemical Journal, 90 (2), 159-163.
[16]         Huang, L.-M.,Deng, C.-B.,Huang, N.Huang, X.-J. (2013). Multivariate statistical approach to identify heavy metal sources in agricultural soil around an abandoned Pb–Zn mine in Guangxi Zhuang Autonomous Region, China. Environmental Earth Sciences, 68 (5), 1331-1348.
[17]         Parizi, H. S.Samani, N. (2013). Geochemical evolution and quality assessment of water resources in the Sarcheshmeh copper mine area (Iran) using multivariate statistical techniques. Environmental Earth Sciences, 69 (5), 1699-1718.
[18]         Singh, C. K.,Shashtri, S.Mukherjee, S. (2011). Integrating multivariate statistical analysis with GIS for geochemical assessment of groundwater quality in Shiwaliks of Punjab, India. Environmental Earth Sciences, 62 (7), 1387-1405.
[19]         Morrison, J. M.,Goldhaber, M. B.,Ellefsen, K. J.Mills, C. T. (2011). Cluster analysis of a regional-scale soil geochemical dataset in northern California. Applied Geochemistry, 26S105-S107.
[20]         Sfidari, E.,Kadkhodaie-Ilkhchi, A.Najjari, S. (2012). Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems. Journal of Petroleum Science and Engineering, 86190-205.
[21]         Beane, R. E.Bodnar, R. J. (1995). hydrothermal fluids and alteration in porphyry copper deposits. Arizona Geological Society Digest, 2083-93.
[22]         Berger, B. R.  (2008).Preliminary model of porphyry copper deposits.US Geological Survey.
[23]         Reimann, C.,Filzmoser, P.Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 17 (3), 185-206.
[24]         Hassanipak, A. A.Sharafodin, M. (2011). Exploratory data analysis. Tehran University Press, Third edition. (In Persian)
[25]         Rencher, A. C.Christensen, W. F.  (2012).Methods of multivariate analysis.John Wiley & Sons.
[26]         Chen, Z.Shixiong, X. ‘K-means Clustering Algorithm with Improved Initial Center’, in Editor (Ed.)^(Eds.): ‘Book K-means Clustering Algorithm with Improved Initial Center’ (2009, edn.), pp. 790-792
[27]         Meshkani, S. A.,Mehrabi, B.,Yaghubpur, A.Alghalandis, Y. F. (2011). The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj–Sirjan metallogenic zone, Iran. Journal of Geochemical Exploration, 108 (3), 183-195.
[28]         Templ, M.,Filzmoser, P.Reimann, C. (2008). Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23 (8), 2198-2213.