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Varieties of approaches and algorithms have been presented to identifythe distribution of elements. Previous researches based on the type of problem,categorized their data in proper clusters or classes. This means that theprocess of solution could be supervised or unsupervised. In cases, where thereis no idea about dependency of samples to specific groups, clustering methodsunsupervised are applied. About geochemistry data, since various elements areinvolved, in addition to the complex nature of geochemical data, clusteringalgorithms would be useful for recognition of elements distribution. In thispaper, Self-Organizing Map SOM algorithm, as an unsupervised method, isapplied for clustering samples based on REEs contents. For this reason theChoghart Fe-REE deposit Bafq district, central Iran, was selected as studyarea and dataset was a collection of 112 lithology samples that were assayedwith laboratory tests such as ICP-MS and XRF analysis. In this study, inputvectors include 19 features which are coordinates x, y, z and concentrations ofREEs as well as the concentration of Phosphate since the apatite is the main source of REEs inthis particular research. Four clusters were determined as an optimal number ofclusters using silhouette criterion as well as k-means clustering method andSOM. Therefore, using self-organizing map, study area was subdivided in fourzones. These four zones can be described as phosphate type, albitofyre type,metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is themost prone to rare earth elements. Eventually, results were validated withlaboratory analysis.


Self Organizing Map SOM, REEs, Geochemistry, Choghart, Central Iran

Cite this paper

Sarparandeh, M. and Hezarkhani, A. 2016 Application of Self-Organizing Map for Exploration of REEs’ Deposition. Open Journal of Geology, 6, 571-582. doi: 10.4236-ojg.2016.67045.

Autor: Mohammadali Sarparandeh, Ardeshir Hezarkhani*



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