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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/gi-2017-38
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
11 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Instrumentation, Methods and Data Systems (GI).
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran
Mohammadali Sarparandeh and Ardeshir Hezarkhani Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, +9821, Iran
Abstract. The use of efficient methods for data processing has always been of interest by researchers in the field of earth science. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of geochemical distribution of REEs needs to use such methods. Especially multivariate nature of REEs data makes it a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of rare earth elements (REEs) in the Kiruna type magnetite–apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with ICP-MS. Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) including modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative), k-means and self-organizing map (SOM) were applied and results were evaluated using silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts, and analysis results such as SEM, XRD, ICP-MS and optical mineralogy. The results of k-means and SOM have the best matches with reality, experimental studies of samples and also field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It concluded that the combination of the proposed methods and geological studies, leads to finding some hidden information and this approach has the best results compared to using only one of them.

Citation: Sarparandeh, M. and Hezarkhani, A.: Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran, Geosci. Instrum. Method. Data Syst. Discuss., https://doi.org/10.5194/gi-2017-38, in review, 2017.
Mohammadali Sarparandeh and Ardeshir Hezarkhani
Mohammadali Sarparandeh and Ardeshir Hezarkhani
Mohammadali Sarparandeh and Ardeshir Hezarkhani

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