Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran
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 (RE...
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ftdoajarticles:oai:doaj.org/article:845e45ee973a49d88000012d37a76520 2023-05-15T17:04:20+02:00 Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran M. Sarparandeh A. Hezarkhani 2017-12-01T00:00:00Z https://doi.org/10.5194/gi-6-537-2017 https://doaj.org/article/845e45ee973a49d88000012d37a76520 EN eng Copernicus Publications https://www.geosci-instrum-method-data-syst.net/6/537/2017/gi-6-537-2017.pdf https://doaj.org/toc/2193-0856 https://doaj.org/toc/2193-0864 doi:10.5194/gi-6-537-2017 2193-0856 2193-0864 https://doaj.org/article/845e45ee973a49d88000012d37a76520 Geoscientific Instrumentation, Methods and Data Systems, Vol 6, Pp 537-546 (2017) Geophysics. Cosmic physics QC801-809 article 2017 ftdoajarticles https://doi.org/10.5194/gi-6-537-2017 2022-12-31T13:34:55Z 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 ... Article in Journal/Newspaper Kiruna Directory of Open Access Journals: DOAJ Articles Kiruna Geoscientific Instrumentation, Methods and Data Systems 6 2 537 546 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Geophysics. Cosmic physics QC801-809 |
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Geophysics. Cosmic physics QC801-809 M. Sarparandeh A. Hezarkhani Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
topic_facet |
Geophysics. Cosmic physics QC801-809 |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
M. Sarparandeh A. Hezarkhani |
author_facet |
M. Sarparandeh A. Hezarkhani |
author_sort |
M. Sarparandeh |
title |
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
title_short |
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
title_full |
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
title_fullStr |
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
title_full_unstemmed |
Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran |
title_sort |
application of unsupervised pattern recognition approaches for exploration of rare earth elements in se-chahun iron ore, central iran |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/gi-6-537-2017 https://doaj.org/article/845e45ee973a49d88000012d37a76520 |
geographic |
Kiruna |
geographic_facet |
Kiruna |
genre |
Kiruna |
genre_facet |
Kiruna |
op_source |
Geoscientific Instrumentation, Methods and Data Systems, Vol 6, Pp 537-546 (2017) |
op_relation |
https://www.geosci-instrum-method-data-syst.net/6/537/2017/gi-6-537-2017.pdf https://doaj.org/toc/2193-0856 https://doaj.org/toc/2193-0864 doi:10.5194/gi-6-537-2017 2193-0856 2193-0864 https://doaj.org/article/845e45ee973a49d88000012d37a76520 |
op_doi |
https://doi.org/10.5194/gi-6-537-2017 |
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Geoscientific Instrumentation, Methods and Data Systems |
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6 |
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