Journal of Economic Geology (Jul 2017)
Modeling the geochemical distribution of rare earth elements (REEs) using multivariate statistics in the eastern part of Marvast placer, the Yazd province
Abstract
Introduction Nowadays, exploration of rare earth element (REE) resources is considered as one of the strategic priorities, which has a special position in the advanced and intelligent industries (Castor and Hedrick, 2006). Significant resources of REEs are found in a wide range of geological settings, including primary deposits associated with igneous and hydrothermal processes (e.g. carbonatite, (per) alkaline-igneous rocks, iron-oxide breccia complexes, scarns, fluorapatite veins and pegmatites), and secondary deposits concentrated by sedimentary processes and weathering (e.g. heavy-mineral sand deposits, fluviatile sandstones, unconformity-related uranium deposits, and lignites) (Jaireth et al., 2014). Recent studies on various parts of Iran led to the identification of promising potential of these elements, including Central Iran, alkaline rocks in the Eslami Peninsula, iron and apatite in the Hormuz Island, Kahnouj titanium deposit, granitoid bodies in Yazd, Azerbaijan, and Mashhad and associated dikes, and finally placers related to the Shemshak formation in Marvast, Kharanagh, and Ardekan indicate high concentration of REE in magmatogenic iron–apatite deposits in Central Iran and placers in Marvast area in Yazd (Ghorbani, 2013). Materials and methods In the present study, the geochemical behavior of rare earth elements is modeled by using multivariate statistical methods in the eastern part of the Marvast placer. Marvast is located 185 km south of the city of Yazd in central Iran between Yazd and Mehriz. This area lies within the southeastern part of the Sanandaj-Sirjan Zone (Alipour-Asll et al., 2012). The samples of 53 wells were analyzed for Whole-rock trace-element concentrations (including REE) by inductively coupled plasma-mass spectrometry (ICP-MS) (GSI, 2004). The clustering techniques such as multivariate statistical analysis technique can be employed to find appropriate groups in data sets. One of the main objectives of data clustering is to maximize both the similarity within each cluster and the difference between clusters, and finally find the structure in the data. Nowadays, cluster analysis is applied in many disciplines: biology, botany, medicine, psychology, geography, marketing, image processing, psychiatry, archaeology, etc. (Everitt et al., 2011). To execute a partitioning algorithm, the principal components analysis (PCA) algorithm is applied for feature selection, feature extraction and dimension reduction. Hierarchical clustering can be utilized to provide a nested sequence of partitions with bottom-up or top-down methods based on similarity. The single linkage and complete linkage are the most popular hierarchical algorithms (Jain et al., 1999; Ji et al., 2007). Results and discussion The REE chondrite-normalized pattern for the eastern area in the Marvast placer represents a high match to the standard pattern of monazite. This pattern shows the positive anomaly of Ce and the negative anomaly of Eu. To determine the distribution of REEs concentration, 2D interpolation maps were plotted in three groups of light, middle, and heavy REEs (LREE, MREE, and HREE), which were indicated in the geochemical anomaly at the south and south-west of the area. The relative ratios of (LREE/HREE) and (Ce/Eu) exposed the high proportion of LREEs to HREEs. In the next section, the hierarchical clustering algorithm was employed to partition the data in the feature and sample levels. The elements portioning demonstrated four separated groups, which can be related to atomic and chemical structures. The studied region was divided into four zones by the clustering approach. The fourth zone confine coincided with the REE anomaly area. Finally, PCA was applied as the multivariate statistical tool to this dataset. Hence three principal components modeled over 90% of the variance. For the first component, the distribution map of load factor has a good agreement with anomaly area. References Alipour-Asll, M., Mirnejad, H. and Milodowski, A.E., 2012. Occurrence and paragenesis of diagenetic monazite in the upper Triassic black shales of the Marvast region, South Yazd, Iran. Mineralogy and Petrology, 104(3-4): 197-210. Castor, S.B. and Hedrick, J.B., 2006. Rare earth elements. In: J.E. Kogel (Editors), Industrial minerals & rocks: commodities, markets, and uses. Society for mining, metallurgy, and exploration, Inc. (SME), Colorado, pp. 769-792. Everitt, B., Landau, S., Leese, M. and Stahl, D., 2011. Cluster Analysis. 5th ed., Wiley Publishing, Hoboken, Geological Survey of Iran (GSI), 2004. Exploration of rare earth element and monazite in the alluvium of the southern Marvast (eastern area). Tehran, 66 pp. (in Persian) Ghorbani, M., 2013. The economic geology of Iran: mineral deposits and natural resources. Springer, London, 569 pp. Jain, A.K., Murty, M.N. and Flynn, P.J., 1999. Data clustering: a review. Association for Computing Machinery computing surveys, 31(3): 264-323. Jaireth, S., Hoatson D.M. and Miezitis Y., 2014. Geological setting and resources of the major rare-earth-element deposits in Australia. Ore Geology Reviews, 62: 72-128. Ji, H., Zeng, D., Shi, Y., Wu, Y. and Wu, X., 2007. Semi-hierarchical correspondence cluster analysis and regional geochemical pattern recognition. Journal of Geochemical Exploration, 93(2): 109-119.
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