Categorization of Mineral Resources using Random Forest Model in a Copper Deposit in Peru | ||
| Journal of Mining and Environment | ||
| مقاله 9، دوره 16، شماره 3، مرداد و شهریور 2025، صفحه 947-962 اصل مقاله (12.73 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15568.2984 | ||
| نویسندگان | ||
| Marco Antonio Cotrina-Teatino* 1؛ Jairo Jhonatan Marquina-Araujo1؛ Jose Nestor Mamani-Quispe2؛ Solio Marino Arango-Retamozo1؛ Johnny Henrry Ccatamayo-Barrios3؛ Joe Alexis Gonzalez-Vasquez4؛ Teofilo Donaires-Flores2؛ Maxgabriel Alexis Calla-Huayapa5 | ||
| 1Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru | ||
| 2Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru | ||
| 3Department of Mining Engineering, Universidad Nacional San Cristobal de Huamanga, Ayacucho, Peru | ||
| 4Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru | ||
| 5Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, Peru | ||
| چکیده | ||
| This work aimed to categorize mineral resources in a copper deposit in Peru, using a machine learning model, integrating the K-prototypes clustering algorithm for initial classification and Random Forest (RF) as a spatial smoother. A total of 318,443 blocks were classified using geostatistical and geometric variables derived from Ordinary Kriging (OK) such as kriging variance, sample distance, number of drillholes, and geological confidence. The model was trained and validated using precision, recall, and F1-score metrics. The results indicated an overall accuracy of 97%, with the measured category achieving 98% precision and an F1-score of 0.98. The total estimated tonnage was 5,859.36 Mt, distributed as follows: 1,446.13 Mt (measured), 2,249.22 Mt (Indicated), and 2,164.01 Mt (Inferred), with average copper grades of 0.43%, 0.33%, and 0.31% Cu, respectively. Compared to the traditional geostatistical methods, this hybrid approach improves classification objectivity, spatial continuity, and reproducibility, minimizing abrupt transitions between categories. The RF model proved to be a robust tool, reducing classification inconsistencies and better capturing geological uncertainty. Future studies should explore hybrid models (K-means with RF, ANN with K-Prototypes, gradient boosting, and deep learning) and incorporate economic variables to optimize decision-making in resource estimation. | ||
| کلیدواژهها | ||
| Random Forest؛ mineral resource categorization؛ geostatistics؛ kriging variance | ||
| مراجع | ||
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[1]. JORC Code. (2012). Australasian code for reporting of exploration results, mineral resources and ore reserves. AusIMM, 44.
[2]. CIM. (2019). Estimation of mineral resources & mineral reserves best practice guidelines. Canadian Institute of Mining.
[3]. SAMREC. (2016). The South African code for the reporting of exploration results, mineral resources and mineral reserves (the SAMREC Code). South African Mineral Resource Committee.
[4]. CRIRSCO. (2014). International Reporting Template for the Public Reporting of Exploration Results Mineral Resources and Mineral Reserves.
[5]. Stephenson, P., Stoker, P. (2001). Mineral resource and ore reserve estimation - the AusIMM guide to good practice (monograph 23). Miner Eng, 14(9).
[6]. Abzalov, M. (2016). Methodology of the mineral resource classification. Modern Approaches in Solid Earth Sciences, 12, 355-363.
[7]. Owusu, S. (2019). Critical Review of Mineral Resource Classification Techniques in the Gold Mining Industry. Insights in Mining Science & Technology, 1(3), 555564.
[8]. Coombes, J., Fahey, G., Stoker, P. (2014). Overview – classifcation and reporting. Mineral resource and ore reserve estimation—the AusIMM guide to good practice. Australas Inst Min Metall Second Ed Monogr, 30, 767–770.
[9]. Menin, R., Diedrich, C., Reuwsaat, JD., De Paula, WF. (2017). Drilling Grid Analysis for Defining Open-Pit and Underground Mineral Resource Classification through Production Data. Geostatistics Valencia 2016, 19, 271-285.
[10]. Deustch, C., Leauangthong, O., Ortiz, J. (2007). Case for geometric criteria in resources and reserves classifcation. Trans Soc Min Metall Explor, 322.
[11]. Mucha, J., Wasilewska-Błaszczyk, M., Augus̈cik, J. (2015). Categorization of mineral resources based upon geostatistical estimation of the continuity of changes of resource parameters. Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences.
[12]. Nowak, M., Leuangthong, O. (2019). Optimal drill hole spacing for resource classification. Mining Goes Digital - Proceedings of the 39th international symposium on Application of Computers and Operations Research in the Mineral Industry, APCOM 2019.
[13]. Taghvaeenezhad, M., Shayestehfar, M., Moarefvand, P., Rezaei, A. (2020). Quantifying the criteria for classification of mineral resources and reserves through the estimation of block model uncertainty using geostatistical methods: a case study of Khoshoumi Uranium deposit in Yazd, Iran. Geosystem Engineering, 23(4), 216-225.
[14]. Emery, X., Ortiz, JM., Rodríguez, JJ. (2006). Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations. Math Geol, 38, 445-464.
[15]. Madani, N. (2020). Mineral resource classification based on uncertainty measures in geological domains. Springer Series in Geomechanics and Geoengineering, 157-164.
[16]. Silva, DSF., Boisvert, JB. (2014). Mineral resource classification: A comparison of new and existing techniques. J South Afr Inst Min Metall, 114(3).
[17]. Isatelle, F., Rivoirard, J. (2019). Mineral Resources classification of a nickel laterite deposit: Comparison between conditional simulations and specific areas. J South Afr Inst Min Metall, 119(10).
[18]. Rocha, V., Bassani, MA. (2023). Practical application of a multi-layer scorecard workflow (MLSW) for comprehensive mineral resource classification. Applied Earth Science: Transactions of the Institute of Mining and Metallurgy, 132(3-4).
[19]. Dominy, S., Stephenson, P., Annels, A. (2001). Classification and reporting of mineral resources for high-nugget effect gold vein deposits. Exploration and Mining Geology, 10, 215-233.
[20]. Stephenson, PR., Allman, A., Carville, DP., Stoker, PT., Mokos, P., Tyrrell, J., Burrows, T. (2006). Mineral resource classification - It’s time to shoot the ’spotted dog’! Australasian Institute of Mining and Metallurgy Publication Series, 91-96.
[21]. Verly, G., Parker, HM. (2021). Conditional Simulation for Mineral Resource Classification and Mining Dilution Assessment from the Early 1990s to Now. Math Geosci, 53, 279-300.
[22]. Galetakis, M., Vasileiou, A., Rogdaki, A., Deligiorgis, V., Raka, S. (2022). Estimation of Mineral Resources with Machine Learning Techniques. Material proceedings, 5(1), 122.
[23]. Singh, T., Jhariya, DC., Sahu, M., Dewangan, P., Dhekne, PY. (2022). Classifying Minerals using Deep Learning Algorithms. IOP Conf Ser Earth Environ Sci, 1032(2022), 012046.
[24]. Hernández, H. (2024). A semiautomatic multi criteria method for mineral resources classification. Applied Earth Science: Transactions of the Institutions of Mining and Metallurgy, 133, 211-223.
[25]. Afzal, P., Gholami, H., Madani, N., Yasrebi, A., Sadeghi, B. (2023) Mineral Resource Classification Using Geostatistical and Fractal Simulation in the Masjed Daghi Cu–Mo Porphyry Deposit, NW Iran. Minerals, 13(3), 370.
[26]. Battalgazy, N., Madani, N. (2019). Categorization of mineral resources based on different geostatistical simulation algorithms: a case study from an iron ore deposit. Nat Resour Res, 28, 1329-1351.
[27]. Farhadi, S., Tatullo, S., Boveiri Konari, M., Afzal, P. (2024). Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. J Geochem Explor, 260, 107441.
[28]. Afzal, P., Farhadi, S., Konari, MB., Meigoony, MS., Saein, LD. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia, 12(1), 191-199.
[29]. Nwaila, GT., Zhang, SE., Bourdeau, JE., Frimmel, HE., Ghorbani, Y. (2024). Spatial Interpolation Using Machine Learning: From Patterns and Regularities to Block Models. Natural Resources Research, 33, 129-161.
[30]. Salarian, S., Oskooi, B., Mostafaei, K., Smirnov, MY. (2024). Improving the resource modeling results using auxiliary variables in estimation and simulation methods. Earth Sci Inform, 17, 4161-4181.
[31]. Cevik, IS., Leuangthong, O., Caté, A., Ortiz, JM. (2021). On the Use of Machine Learning for Mineral Resource Classification. Min Metall Explor, 38, 2055-2073.
[32]. Ribeiro, DT., Filho, CGM., de Souza, LE., Costa, JFCL., de Almeida, D. (2012). Utilização de critérios geoestatísticos para comparação de malha de sondagem visando à maximização da quantidade de recursos. Revista Escola de Minas, 65(1).
[35]. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov, 2, 283-304.
[34]. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlast, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
[35]. van der Walt, S., Colbert, SC., Varoquaux, G. (2011). The NumPy Array: A Structure for Efficient Numerical Computation. Comput Sci Eng, 13, 22–30.
[36]. Álvarez Cid-Fuentes, J., Álvarez, P., Amela, R., Ishii, K., Morizawa, RK., Badia, RM. (2020). Efficient development of high performance data analytics in Python. Future Generation Computer Systems, 111, 570–581.
[37]. Ahsan, M., Mahmud, M., Saha, P., Gupta, K., Siddique, Z. (2021). Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance. Technologies (Basel), 9, 52.
[38]. Cotrina, M., Marquina, J., Noriega, E., Mamani, J., Ccatamayo, J., Gonzalez, J., Arango, S. (2024). Predicting Open Pit Mine Production using Machine Learning Techniques: A Case Study in Peru. Journal of Mining and Environment, 15(4), 1345–1355.
[39]. Wackernagel, H. (2003). Ordinary Kriging. In: Multivariate Geostatistics. Springer Berlin Heidelberg, Berlin, Heidelberg, 79–88.
[40]. Journel, AG. (1983). Nonparametric estimation of spatial distributions. Journal of the International Association for Mathematical Geology, 15, 445-468.
[41]. Marquina, J., Cotrina, M., Mamani, J., Noriega, E., Vega, J., Cruz, J. (2024). Copper Ore Grade Prediction using Machine Learning Techniques in a Copper Deposit. Journal of Mining and Environment, 15(3), 1011-1027.
[42]. Okwu, MO., Otanocha, OB., Edward, BA., Oreko, BU., Oyekale, J., Oyejide, OJ., Osuji, J., Maware, C., Ezekiel, K., Orikpete, OF. (2024). Investigating the Accuracy of Artificial Neural Network Models in Predicting Surface Roughness in Drilling Processes. Procedia Comput Sci, 232, 1982-1990.
[43]. Marquina-Araujo, JJ., Cotrina-Teatino, MA., Cruz-Galvez, JA., Noriega-Vidal, EM., Vega-Gonzalez, JA. (2024). Application of Autoencoders Neural Network and K-Means Clustering for the Definition of Geostatistical Estimation Domains. Mathematical Modelling of Engineering Problems, 11(5), 1207-1218.
[44]. Carranza, EJM., Laborte, AG. (2015). Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Comput Geosci, 74, 60-70.
[45]. Josso, P., Hall, A., Williams, C., Le Bas, T., Lusty, P., Murton, B. (2023). Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean. Ore Geol Rev, 162, 105671.
[46]. Ford, A. (2020). Practical Implementation of Random Forest-Based Mineral Potential Mapping for Porphyry Cu–Au Mineralization in the Eastern Lachlan Orogen, NSW, Australia. Natural Resources Research, 29, 267-283.
[47]. Lachaud, A., Adam, M., Mišković, I. (2023). Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data. Minerals, 13(8), 1073.
[48]. Markoulidakis, I., Markoulidakis, G. (2024). Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis. Technologies (Basel), 12(7), 113.
[49]. Caelen, O. (2017). A Bayesian interpretation of the confusion matrix. Ann Math Artif Intell, 81, 429-450. | ||
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