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

Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

doi:https://doi.org/10.5194/gi-6-537-2017

Articles | Volume 6, issue 2

https://doi.org/10.5194/gi-6-537-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gi-6-537-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 2

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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Final revised paper (published on 15 Dec 2017)
Preprint (discussion started on 11 Aug 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Comment on the paper of Sarparandeh and Hezarkhani', Mohammad Parsa, 12 Sep 2017
- AC1: 'Reply to comments', Mohammadali Sarparandeh, 12 Sep 2017
RC1: 'Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran.', Amir Mollajan, 20 Sep 2017
- AC2: 'Reply to comments of reviewer', Mohammadali Sarparandeh, 20 Sep 2017
RC2: 'Review', Anonymous Referee #1, 26 Oct 2017
- AC3: 'Reply to Anonymous Referee#1', Mohammadali Sarparandeh, 28 Oct 2017

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.