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Uncertainty assessment in mineral resource estimation using geostatistics and Monte Carlo simulation

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Category: Content №5 2025

Last Updated on 25 October 2025

Published on 30 November -0001

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Authors:

M. Mazari*, orcid.org/0000-0002-7745-6845, University of Bejaia, Faculty of Technology, Mining and Geology Department, Bejaia, Algeria; National Polytechnic School (ENP), Mining Engineering, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

B. Chemali, orcid.org/0009-0009-3090-2092, National Polytechnic School (ENP), Civil Engineering Department, Seismic Engineering and Structural Dynamics Laboratory (LGSDS), Algiers, Algeria

S. Bouabdallah, orcid.org/0000-0002-8540-093X, University of Bejaia, Faculty of Technology, Mining and Geology Department, Bejaia, Algeria

I. Chmielewska, orcid.org/0000-0003-1324-9211, Silesian Centre for Environmental Radioactivity, Central Mining Institute National Research Institute, Katowice, Republic of Poland

A. Benselhoub, orcid.org/0000-0001-5891-2860, University of Vienna, Vienna, Austria; Environmental Research Center (C.R.E), Annaba, Algeria

S. Bellucci, orcid.org/0000-0003-0326-6368, INFN-National Institute of Nuclear Physics, National Laboratories of Frascati, Rome, Italy; National Institute of Materials Physics, Bucharest, Romania

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (5): 032 - 040

https://doi.org/10.33271/nvngu/2025-5/032

Abstract:

Purpose. The accurate estimation of mineral reserves is a critical step in the planning and development of mining operations, particularly for phosphate deposits. This study aims to enhance reserve estimation for the Bled El-Hadba phosphate deposit (eastern Algeria) by combining geostatistical and probabilistic approaches.

Methodology. The proposed method integrates ordinary kriging (OK) with Monte Carlo simulation (MCS) to better evaluate the reserves of the main mineralized layer. Ordinary kriging was applied on a 5  5 m grid to generate estimation maps of P₂O₅ grades and layer thicknesses, serving as a spatial model of the deposit. The kriging outputs (mean and variance) were then used as inputs for a Monte Carlo simulation involving 20 million iterations, preserving the statistical characteristics of the original data.

Findings. The combined use of OK and MCS reduced the fluctuations typically observed in kriging-based estimates and led to more stable and robust reserve values. The application of P90 and P50 probability categories contributed to more conservative and reliable classifications of mineral reserves, improving the assessment of proven and probable categories essential for economic decision-making.

Originality. This study introduces a novel integration of kriging with large-scale Monte Carlo simulation to manage uncertainty in phosphate reserve estimation. The approach enables a more precise characterization of spatial variability and supports probabilistic interpretations of resource quantities.

Practical value. The methodology offers a valuable tool for mine planners and decision-makers by improving confidence in reserve estimates and supporting sound economic evaluations. Its application to the Bled El-Hadba deposit demonstrates its effectiveness and potential for broader use in phosphate mining projects.

Keywords: geostatistics, kriging, Monte Carlo simulation, phosphate, resource estimation, uncertainty

References.

1. Jafrasteh, B., Fathianpour, N., & Suárez, A. (2018). Comparison of machine learning methods for copper ore grade estimation. Computational Geosciences, 22, 1371-1388. https://doi.org/10.1007/s10596-018-9758-0

2. Dubiński, J. (2013). Sustainable development of mining mineral resources. Journal of Sustainable Mining, 12, 1-6.

3. Dumakor-Dupey, N. K., & Arya, S. (2021). Machine learning ‒ a review of applications in mineral resource estimation. Energies, 14(14), p. 4079. https://doi.org/10.3390/en14144079

4. Marjoribanks, R. (2010). Geological methods in mineral exploration and mining. Springer Science & Business Media.

5. Zuo, R., Wang, J., Xiong, Y., & Wang, Z. (2021). The processing methods of geochemical exploration data: past, present, and future. Applied Geochemistry, 132, 105072. https://doi.org/10.1016/j.apgeochem.2021.105072

6. Chiles, J.-P., & Delfiner, P. (2012). Geostatistics: modeling spatial uncertainty, (713). John Wiley & Sons.

7. ORGM (2015). Rapport Final sur les Resultats des Travaux D’exploration des Phosphates du Gisement de Bled el Hadba 2012–2014. National Office of Geological and Mining Research. Algeria Boumerdes.

8. DMT-Consulting, G. (2016). Etude de Faisabilité de Bancable du gisement de Bled El Hadba. DMT Group Companies

9. Bassani, M. A., Costa, J. F. C., & Deutsch, C. V. (2024). A Comparative Study Between the Direct and Indirect Methods in Geostatistical Simulation. Mining, Metallurgy & Exploration, 41(6), 3669-3691.

10.      Remy, N. (2009). Applied geostatistics with SGeMS: A user’s guide. Cambridge University Press

11.      Krzemień, A., Fernández, P. R., Sánchez, A. S., & Álvarez, I. D. (2016). Beyond the pan-european standard for reporting of exploration results, mineral resources and reserves. Resources Policy, 49, 81-91. https://doi.org/10.1016/j.resourpol.2016.04.008

12.      Kasmaee, S., Gholamnejad, J., Yarahmadi, A., & Mojtahedzadeh, H. (2010). Reserve estimation of the high phosphorous stockpile at the Choghart iron mine of Iran using geostatistical modeling. Mining Science and Technology, 20(6), 855-860. https://doi.org/10.1016/S1674-5264(09)60295-7

13.      Akbar, D. A. (2012). Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method. International Journal of Mining Science and Technology, 22(4), 573-577. https://doi.org/10.1016/j.ijmst.2012.01.022

14.      Mazari, M., Chabou-Mostefai, S., Bali, A., Kouider, K., Benselhoub, A., & Bellucci, S. (2023). Mineral resource assessment through geostatistical analysis in a phosphate deposit. Naukovyi VisnykNatsionalnoho Hirnychoho Universytetu, (5), 141-147. https://doi.org/10.33271/nvngu/20235/141

15.      Bezzi, N., Aïfa, T., Merabet, D., & Pivan, J.-Y. (2008). Magnetic properties of the Bled El Hadba phosphate-bearing formation (Djebel Onk, Algeria): Consequences of the enrichment of the phosphate ore deposit. Journal of African Earth Sciences, 50(2-4), 255-267. https://doi.org/10.1016/j.jafrearsci.2007.09.019

16.      Marques, D. M., Rubio, R. H., Costa, J. F. C. L., & ­Silva, E. M. A. d. (2014). The effect of accumulation in 2D estimates in phosphatic ore. Rem: Revista Escola de Minas, 67, 431-437.

17.      Yasojima, C., Protázio, J., Meiguins, B., Neto, N., & Morais, J. (2019). A new methodology for automatic cluster-based kriging using K-nearest neighbor and genetic algorithms. Information, 10(11), 357. https://doi.org/10.3390/info10110357

18.      Sarma, D. D. (2010). Geostatistics with applications in earth sciences. Springer Science & Business Media

19.      Choi, S.-K., Canfield, R. A., & Grandhi, R. V. (2007). Reliability-based Structural Design. Springer London.

20.      Kalla, S., & White, C. D. (2007). Efficient design of reservoir simulation studies for development and optimization. SPE Reservoir Evaluation & Engineering, 10 (06), 629-637. https://doi.org/10.2118/95456-PA

21.      Wang, L., Xiao, T., Liu, S., Zhang, W., Yang, B., & Chen, L. (2023). Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation. Gondwana Research, 123, 27-40. https://doi.org/10.1016/j.gr.2023.03.006

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