Araştırma Makalesi

Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

Cilt: 24 Sayı: 1 1 Mart 2021
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Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

Öz

Areal mineral maps are constructed from the polished sections of particles that settle to the bottom of epoxy resin. However, heavy minerals can preferentially settle to the bottom, making the polished surface rich in heavy minerals. Therefore, polished sections will become biased estimates of the volumetric (3D) map. The study aims to test whether any vertical cross-section (any section along the settling direction of particles) can be an unbiased estimate of the 3D mineral map of a chromite ore sample. For the purpose of this study, 2D maps of the vertical cross-sections were acquired by using Random Forest classification coupled with image pre- and post-processing tools. Then, 3D mineral maps were converted from 2D maps without assuming stereological errors. The modal mineralogy and particle size distributions predicted from 3D maps were compared with the same features estimated from the particulate sample by XRD and dry sieving analyses, respectively. Any 2D map which yields a modal mineralogy and a size distribution similar to the true analyses was selected as an unbiased estimate of the true 3D map. The results suggest that any vertical cross-section is an unbiased estimate, unlike polished section at which heavier minerals settle preferentially.

Anahtar Kelimeler

Destekleyen Kurum

BAP institutional fund of the Middle East Technical University

Proje Numarası

BAP-07-02-2014-007-466

Kaynakça

  1. [1] Chatterjee S., Bandopadhyay S. and Machuca D., " Ore grade prediction using a genetic algorithm and clustering Based ensemble neural network model ", Mathematical Geosciences, 42: 309 -326, (2010).
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  4. [4] Perez CA, Estévez PA, Vera PA, Castillo LE, Aravena CM, Schulz DA and Medina LE, " Ore grade estimation by feature selection and voting using boundary detection in digital image analysis ", International Journal of Mineral Processing, 101: 28 -36, (2011).
  5. [5] Singh N., Singh T., Tiwary A. and Sarkar K., " Textural identification of basaltic rock mass using image processing and neural network ", Computational Geosciences, 14: 301 -310, (2010).
  6. [6] Singh V. and Rao SM, " Application of image processing in mineral industry: a case study of ferruginous manganese ores ", Mineral Processing and Extractive Metallurgy (Trans. Inst. Min Met. C)., 115: 155 -160, (2006).
  7. [7] Wang W., " Rock particle image segmentation and systems ", Pattern Recognition Techniques Technology and Applications, In-Teh, Crotia, (2008).
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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

1 Mart 2021

Gönderilme Tarihi

6 Ağustos 2019

Kabul Tarihi

22 Şubat 2020

Yayımlandığı Sayı

Yıl 2021 Cilt: 24 Sayı: 1

Kaynak Göster

APA
Camalan, M., & Çavur, M. (2021). Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map. Politeknik Dergisi, 24(1), 113-120. https://doi.org/10.2339/politeknik.602688
AMA
1.Camalan M, Çavur M. Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map. Politeknik Dergisi. 2021;24(1):113-120. doi:10.2339/politeknik.602688
Chicago
Camalan, Mahmut, ve Mahmut Çavur. 2021. “Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map”. Politeknik Dergisi 24 (1): 113-20. https://doi.org/10.2339/politeknik.602688.
EndNote
Camalan M, Çavur M (01 Mart 2021) Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map. Politeknik Dergisi 24 1 113–120.
IEEE
[1]M. Camalan ve M. Çavur, “Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map”, Politeknik Dergisi, c. 24, sy 1, ss. 113–120, Mar. 2021, doi: 10.2339/politeknik.602688.
ISNAD
Camalan, Mahmut - Çavur, Mahmut. “Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map”. Politeknik Dergisi 24/1 (01 Mart 2021): 113-120. https://doi.org/10.2339/politeknik.602688.
JAMA
1.Camalan M, Çavur M. Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map. Politeknik Dergisi. 2021;24:113–120.
MLA
Camalan, Mahmut, ve Mahmut Çavur. “Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map”. Politeknik Dergisi, c. 24, sy 1, Mart 2021, ss. 113-20, doi:10.2339/politeknik.602688.
Vancouver
1.Mahmut Camalan, Mahmut Çavur. Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map. Politeknik Dergisi. 01 Mart 2021;24(1):113-20. doi:10.2339/politeknik.602688

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