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Volume 15
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10.3390/min15020131
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging

by
Mujigela Maniteja
1,
Gopinath Samanta
2,
Angesom Gebretsadik
3,4,*,
Ntshiri Batlile Tsae
5,*,
Sheo Shankar Rai
1,6,
Yewuhalashet Fissha
5,6,
Natsuo Okada
3 and
Youhei Kawamura
3
1
Department of Mining Engineering, IIT (Indian School of Mines), Dhanbad 826004, Jharkhand, India
2
Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, West Bengal, India
3
Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, Japan
4
Department of Mining Engineering, Aksum University, Aksum-7080, Tigray, Ethiopia
5
Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan
6
Department of Sustainable Energy Engineering, Indian Institute of Technology, Kanpur 208016, Uttar Pradesh, India
*
Authors to whom correspondence should be addressed.
Minerals 2025, 15(2), 131; https://doi.org/10.3390/min15020131
Submission received: 8 December 2024 / Revised: 24 January 2025 / Accepted: 28 January 2025 / Published: 29 January 2025
(This article belongs to the Section Mineral Exploration Methods and Applications)
Versions Notes

Abstract

Mineral grade estimation is a vital phase in mine planning and design, as well as in the mining project’s economic assessment. In mining, commonly accepted methods of ore grade estimation include geometrical approaches and geostatistical techniques such as kriging, which effectively capture the spatial grade variation within a deposit. The application of machine-learning (ML) techniques has been explored in the estimation of mineral resources, where complex correlations need to be captured. In this paper, the authors developed four machine-learning regression models, i.e., support vector regression (SVR), random forest regression (RFR), k-nearest neighbour (KNN) regression, and extreme gradient boost (XGBoost) regression, using a geological database to predict the grade in an Indian iron ore deposit. When compared with ordinary kriging (R2 = 0.74; RMSE = 2.09), the RFR (R2 = 0.74; RMSE = 2.06), XGBoost (R2 = 0.73; RMSE = 2.12), and KNN (R2 = 0.73; RMSE = 2.11) regression models produced similar results. The block model predictions generated using the RFR, XGBoost, and KNN models show comparable accuracy and spatial trends to those of ordinary kriging, whereas SVR was identified as less effective. When integrated with geological methods, these models demonstrate significant potential for enhancing and optimizing mine planning and design processes in similar iron ore deposits.
Keywords: ore grade estimation; iron ore; geostatistics; variogram; ordinary kriging (OK); machine learning (ML); random forest regression (RFR); k-nearest neighbour (KNN) regression; support vector machine; extreme gradient boost (XGBoost) regression ore grade estimation; iron ore; geostatistics; variogram; ordinary kriging (OK); machine learning (ML); random forest regression (RFR); k-nearest neighbour (KNN) regression; support vector machine; extreme gradient boost (XGBoost) regression

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MDPI and ACS Style

Maniteja, M.; Samanta, G.; Gebretsadik, A.; Tsae, N.B.; Rai, S.S.; Fissha, Y.; Okada, N.; Kawamura, Y. Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging. Minerals 2025, 15, 131. https://doi.org/10.3390/min15020131

AMA Style

Maniteja M, Samanta G, Gebretsadik A, Tsae NB, Rai SS, Fissha Y, Okada N, Kawamura Y. Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging. Minerals. 2025; 15(2):131. https://doi.org/10.3390/min15020131

Chicago/Turabian Style

Maniteja, Mujigela, Gopinath Samanta, Angesom Gebretsadik, Ntshiri Batlile Tsae, Sheo Shankar Rai, Yewuhalashet Fissha, Natsuo Okada, and Youhei Kawamura. 2025. "Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging" Minerals 15, no. 2: 131. https://doi.org/10.3390/min15020131

APA Style

Maniteja, M., Samanta, G., Gebretsadik, A., Tsae, N. B., Rai, S. S., Fissha, Y., Okada, N., & Kawamura, Y. (2025). Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging. Minerals, 15(2), 131. https://doi.org/10.3390/min15020131

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