Studying distribution of rare earth elements by classifiers, Se-Chahun iron ore, Central Iran

2016 ◽  
Vol 36 (2) ◽  
pp. 232-239
Author(s):  
Mohammadali Sarparandeh ◽  
Ardeshir Hezarkhani
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Iron Ore ◽  
Central Iran

Geochemistry of major and rare earth elements in garnet of the Kal-e Kafi skarn, Anarak Area, Central Iran: Constraints on processes in a hydrothermal system

2016 ◽  
Vol 54 (5) ◽  
pp. 423-438 ◽  
Author(s):  
S. Ranjbar ◽  
S. M. Tabatabaei Manesh ◽  
M. A. Mackizadeh ◽  
S. H. Tabatabaei ◽  
O. V. Parfenova
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Hydrothermal System ◽  
Central Iran

Separation and continuous determination of the light rare earth elements and thorium in Baotou Iron Ore by a micro-column

2009 ◽  
Vol 281 (3) ◽  
pp. 647-651 ◽  
Author(s):  
Guo-long Guo ◽  
Ming-biao Luo ◽  
Jing-jing Xu ◽  
Teng-xiang Wang ◽  
Rong Hua ◽  
...  
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Iron Ore ◽  
Light Rare Earth ◽  
Light Rare Earth Elements ◽  
Continuous Determination

scholarly journals Controls on the distribution of arsenic and rare earth elements in groundwaters of the Bafgh city area, central Iran

2017 ◽  
Vol 18 (5) ◽  
pp. 1590-1597
Author(s):  
Kaveh Pazand
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Central Iran ◽  
Groundwater Samples ◽  
Tube Wells ◽  
Heavy Rare Earth Elements ◽  
Geological Origin ◽  
Gd Anomaly ◽  
Flat Profile ◽  
Light Rare Earth Elements

Abstract High arsenic (As) contents in groundwater were found in the Bafgh area in central Iran and chosen for hydrogeochemical study. A total of 20 groundwater samples were collected from existing tube wells in the study areas in 2013 and analyzed. The water chemistry is predominantly of Na–Cl type, with concentrations of dissolved As in the range between 0.9 and 74.7 μg/L. The chondrite-normalized rare earth elements patterns exhibited a flat profile, positive Gd anomaly with a predominance of light rare earth elements (LREEs) over heavy rare earth elements (HREEs), suggest that they originated from the same source. The metals in the groundwater of the region have a geological origin.

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

2017 ◽  
Vol 6 (2) ◽  
pp. 537-546 ◽  
Author(s):  
Mohammadali Sarparandeh ◽  
Ardeshir Hezarkhani
Keyword(s):  
Pattern Recognition ◽  
Rare Earth ◽  
Rare Earth Elements ◽  
Iron Ore ◽  
Experimental Studies ◽  
Geochemical Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Hidden Information ◽  
Icp Ms

Abstract. The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite–apatite deposit of Se-Chahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (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 a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and self-organizing map (SOM) – were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is 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.

scholarly journals Principal component analysis of rare earth elements in Sechahun iron deposit, central Iran.

2018 ◽  
Vol 6 (2) ◽  
pp. 205
Author(s):  
Mohammadali Sarparandeh ◽  
Ardeshir Hezarkhani
Keyword(s):  
Principal Component Analysis ◽  
Rare Earth ◽  
Rare Earth Elements ◽  
Precious Metals ◽  
Principal Component ◽  
Component Analysis ◽  
Central Iran ◽  
Iron Deposit ◽  
Geochemical Exploration ◽  
Specific Element

Principal component analysis (PCA) is a sufficient way for finding the groups of correlated features. In geochemical exploration of precious metals, it helps to cluster the elements. Especially for rare earth elements (REEs), because of multiplicity of parameters, the proposed method helps to have a better interpretation. Geochemical exploration programs aim to find the hidden information about specific element(s), its abundance, its behavior and its relation with minerals and some other elements. REEs are a group of elements with same chemical behavior. However, some chemical characteristics of light rare earth elements (LREEs) and heavy rare earth elements (HREEs) are different. In this study, relationship between these elements was investigated by applying PC analysis method in Kiruna-type iron ore deposit of Se-Chahun in Central Iran. The four first PCs covered the most variances of the REEs. All the elements showed a correlation together with exception of La, Ce, Nd, Yb and Y. Results of PC analysis are related to the anomaly of Rare earth elements. It can be concluded that in anomalous areas, loadings of the principal components are affected by variance and anomalous content of the elements.  

Geochemical anomaly recognition of rare earth elements using multi-fractal modeling correlated with geological features, Central Iran

2017 ◽  
Vol 181 ◽  
pp. 318-332 ◽  
Author(s):  
Masoumeh Khalajmasoumi ◽  
Behnam Sadeghi ◽  
Emmanuel John M. Carranza ◽  
Martiya Sadeghi
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Central Iran ◽  
Geochemical Anomaly ◽  
Fractal Modeling ◽  
Geological Features

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

2017 ◽  
Author(s):  
Mohammadali Sarparandeh ◽  
Ardeshir Hezarkhani
Keyword(s):  
Pattern Recognition ◽  
Rare Earth ◽  
Rare Earth Elements ◽  
Iron Ore ◽  
Experimental Studies ◽  
Geochemical Data ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Hidden Information ◽  
Icp Ms

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.

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