Articles | Volume 6, issue 2
https://doi.org/10.5194/gi-6-537-2017
https://doi.org/10.5194/gi-6-537-2017
Research article
 | 
15 Dec 2017
Research article |  | 15 Dec 2017

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Mohammadali Sarparandeh and Ardeshir Hezarkhani

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Cited articles

Abedi, M., Norouzi, G. H., and Torabi, S. A.: Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit, Arab. J. Geosci., 10, 3601–3613, 2012.
Bonyadi, Z., Davidson, G. J. Mehrabi, B., Meffre, S., and Ghazban, F.: Significance of apatite REE depletion and monazite inclusions in the brecciated Se–Chahun iron oxide apatite deposit, Bafq district, Iran: insights from para-genesis and geochemistry, Chem. Geol., 281, 253–269, 2011.
Du, K. L. and Swamy, M. N. S.: Neural Networks in a Softcomputing Framework, Springer-Verlag, London, 194, 2006.
Ellefsen, K. J. and Smith, D. B.: Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model, Appl. Geochem., 75, 200–210, https://doi.org/10.1016/j.apgeochem.2016.05.016, 2016.
Engelbrecht, A., P.: Computational Intelligence, Wiley, Chichester, 63–73, 2002.
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Short summary
Successful clustering of a dataset which is consistent with geological facts and laboratory and field studies is one of the results of this study. Since only REEs were used in this division, a good agreement of the results with lithology is considerable. Results show that unsupervised pattern recognition helps find some hidden information which would be difficult to obtain in usual ways. In addition, methods presented in this study will enable better interpretation of data.