Mineral resource assessment through geostatistical analysis in a phosphate deposit
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- Category: Content №5 2023
- Last Updated on 27 October 2023
- Published on 30 November -0001
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Authors:
M.Mazari*1,2, orcid.org/0000-0002-7745-6845, Department of Mining Engineering, National Polytechnic School of Algiers, Algeria, Algeria; Department of mines and geology, Faculty of Technology, Abderrahmene Mira University, Bejaia, Algeria
S.Chabou-Mostefai1, orcid.org/0000-0001-7024-7066, Department of Mining Engineering, National Polytechnic School of Algiers, Algeria, Algeria
A.Bali3, orcid.org/0000-0002-4699-849X, Department of civil Engineering, Laboratory of Civil Engineering Materials and Environment “LMGCE”, National Polytechnic School of Algiers, Algeria, Algeria
K.Kouider4,5, orcid.org/0009-0005-0553-6116, Water Sciences Research Laboratory-National Polytechnic School of Algiers, Algeria, Algeria; Faculty of Civil Engineering, University of Sciences and Technology Houari Boumediene Algiers, Algeria, Algeria
A.Benselhoub6, orcid.org/0000-0001-5891-2860, Environment, Modeling and Climate Change Division, Environmental Research Center (C.R.E), Annaba, Algeria
S.Bellucci7, orcid.org/0000-0003-0326-6368, INFN-Frascti National Laboratories, Frascati, Rome, Italy
* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (5): 141 - 147
https://doi.org/10.33271/nvngu/2023-5/141
Abstract:
Purpose. The selection of an appropriate variographic model is crucial in geostatistics to obtain accurate estimates of mineral reserves. The aim of this work is to develop a reserve estimation tool using a geostatistical approach.
Methodology. The geostatistical approach is based on selecting the most representative variographic models for the studied variables. The model selection is done by applying a cross-validation procedure leave-one-out (LOOCV). LOOCV is a resampling technique used in statistical analysis and machine learning to estimate the generalization error of a model and compare the performance of different models. The studied variables are then estimated using ordinary kriging.
Findings. The application of the proposed approach has resulted in satisfactory results in terms of dispersion of grades and thicknesses of mineralized layers in a phosphate deposit. To evaluate the quality of the adjustment models obtained, efficiency factors such as Nash-Sutcliffe, and RMSE (Root Mean Square Error), were employed. These factors provide quantitative measures of the agreement between the observed and predicted values. The NSE (Nash-Sutcliffe efficiency) and RMSE (root mean square error) values of 0.572 and 6.599, respectively, indicate a better fit and greater accuracy of the adjustment models. The accuracy and efficiency criteria of the studied variables have acceptable values, with a mean square error (MSE) of 1.54 · 10-7.
Originality. The combination of the least squares and LOOCV methods in the geostatistical analysis leads to improved estimation precision, greater reliability in representing the spatial variability of the parameters, and enhanced confidence in the validity of the adjustment models.
Practical value. The development of a computer code for this geostatistical approach provides a practical tool for decision-makers to use in the management and exploitation of mining sites. Overall, this study has contributed to the advancement of geostatistical techniques and their application in the mining industry.
Keywords: Bled el Hadba deposit, cross validation (LOOCV), geostatistics, kriging, mineral reserves
References.
1. Ajak, A. D., Lilford, E., & Topal, E. (2018). Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty. Resources Policy, 55, 62-79. https://doi.org/10.1016/j.resourpol.2017.10.016.
2. Li, Z., Zhang, X., Zhu, R., Zhang, Z., & Weng, Z. (2020). Integrating data-to-data correlation into inverse distance weighting. Computational Geosciences, 24, 203-216. https://doi.org/10.1007/s10596-019-09913-9.
3. Choi, Y., Baek, J., & Park, S. (2020). Review of GIS-based applications for mining: Planning, operation, and environmental management. Applied Sciences, 10(7), 2266. https://doi.org/10.3390/app10072266.
4. Jalloh, A. B., Kyuro, S., Jalloh, Y., & Barrie, A. K. (2016). Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study. International Journal of Mining Science and Technology, 26(4), 581-585. https://doi.org/10.1016/j.ijmst.2016.05.008.
5. Rebbah, R., Duarte, J., Djezairi, O., Fredj, M., & Baptista, J. S. (2021). A Tunnel under an In-Pit Mine Waste Dump to Improve Environmental and Landscape Recovery of the Site. Minerals, 11(6). https://doi.org/10.3390/min11060566.
6. Zerzour, O., Gadri, L., Hadji, R., Mebrouk, F., & Hamed, Y. (2021). Geostatistics-Based Method for Irregular Mineral Resource Estimation, in Ouenza Iron Mine, Northeastern Algeria. Geotechnical and Geological Engineering, 39(5), 3337-3346. https://doi.org/10.1007/s10706-021-01695-1.
7. Afeni, T. B., Akeju, V. O., & Aladejare, A. E. (2021). A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit. Geoscience Frontiers, 12(1), 243-253. https://doi.org/10.1016/j.gsf.2020.02.019.
8. Berrar, D. (2019). Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology, (1), 542-545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X.
9. Vehtari, A., Simpson, D. P., Yao, Y., & Gelman, A. (2018). Limitations of “Limitations of Bayesian Leave-one-out Cross-Validation for Model Selection”. Computational Brain & Behavior, 2(1), 22-27. https://doi.org/10.1007/s42113-018-0020-6.
10. Giordano, R., Stephenson, W., Liu, R., Jordan, M., & Broderick, T. (2019). A Swiss Army Infinitesimal Jackknife. In C. Kamalika, & S. Masashi (Eds.). Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR: Proceedings of Machine Learning Research, (pp. 1139-1147). Retrieved from http://proceedings.mlr.press/v89/giordano19a/giordano19a.pdf.
11. Kechiched, R., Laouar, R., Bruguier, O., Salmi-Laouar, S., Kocsis, L., Bosch, D., …, & Larit, H. (2018). Glauconite-bearing sedimentary phosphorites from the Tébessa region (eastern Algeria): Evidence of REE enrichment and geochemical constraints on their origin. Journal of African Earth Sciences, 145, 190-200. https://doi.org/10.1016/j.jafrearsci.2018.05.018.
12. Zhongda, T., Shujiang, L., Yanhong, W., & Xiangdong, W. (2018). Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems. Transactions of the Institute of Measurement and Control, 40(16), 4382-4396. https://doi.org/10.1177/0142331217748193.
13. Duc, L., & Sawada, Y. (2022). A signal processing-based interpretation of the Nash-Sutcliffe efficiency. EGUsphere. https://doi.org/10.5194/egusphere-2022-955.
14. Chen, C., Zhang, Q., Kashani, M. H., Jun, C., Bateni, S. M., Band, S. S., Dash, S. S., & Chau, K.-W. (2022). Forecast of rainfall distribution based on fixed sliding window long short-term memory. Engineering Applications of Computational Fluid Mechanics, 16(1), 248-261. https://doi.org/10.1080/19942060.2021.2009374.
15. Zhong, X., & Dutta, U. (2015). Engaging Nash-Sutcliffe efficiency and model efficiency factor indicators in selecting and validating effective light rail system operation and maintenance cost models. Journal of Traffic and Transportation Engineering, 3, 255-265. https://doi.org/10.17265/2328-2142/2015.05.001.
16. 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.
17. Zeybek, M. (2018). Nash-sutcliffe efficiency approach for quality improvement. Journal of Applied Mathematics and Computation, 2(11), 496-503. https://doi.org/10.26855/jamc.2018.11.001.
18. Magnusson, M., Vehtari, A., Jonasson, J., & Andersen, M. (2020). Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. In C. Silvia, & C. Roberto (Eds.). Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR: Proceedings of Machine Learning Research, (pp. 341-351). Retrieved from http://proceedings.mlr.press/v108/magnusson20a/magnusson20a.pdf.
19. Gronau, Q. F., & Wagenmakers, E.-J. (2019). Limitations of Bayesian leave-one-out cross-validation for model selection. Computational brain & behavior, (2), 1-11. https://doi.org/10.1007/s42113-018-0011-7.
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