[1] Coates, D. (1985). Mineral resources. In Geology and Society, 19–46.
[2] Dubiński, J. (2013). Sustainable Development of Mining Mineral Resources. Journal of Sustainable Mining, 12(1), 1-6. doi:https://doi.org/10.7424/jsm130102.
[3] Ericsson, M., Löf, O. (2019). Mining’s contribution to national economies between 1996 and 2016. Mineral Economics, 32, 223-250. doi:https://doi.org/10.1007/s13563-019-00191-6.
[4] Van Gosen, B., Verplanck, P., Long, K., Gambogi, J., Seal, R. (2014). The Rare-Earth Elements: Vital to Modern Technologies and Lifestyles. US Geological Survey: Reston, VA, USA.
[5] Henckens, MLCM., Biermann, FHB., Driessen, PPJ. (2019). Mineral resources governance: A call for the establishment of an International Competence Center on Mineral Resources Management. Resour Conserv Recycl, 141, 255-263. doi:https://doi.org/10.1016/j.resconrec.2018.10.033.
[6] Crowson, PCF. (2011). Mineral reserves and future minerals availability. Mineral Economics, 24, 1-6. doi:https://doi.org/10.1007/s13563-011-0002-9.
[7] 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, 3337-3346. doi:https://doi.org/10.1007/s10706-021-01695-1.
[8] Hartman, HL., Mutmansky, JM. (2002). Introductory mining engineering. Introductory Mining Engineering.
[9] CIM. (2019). Estimation of mineral resources & mineral reserves best practice guidelines. Canadian Institute of Mining.
[10] JORC Code. (2012). Australasian code for reporting of exploration results, mineral resources and ore reserves. AusIMM 44.
[11] 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.
[12] Goodfellow, RC., Dimitrakopoulos, R. (2016). Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing Journal, 40, 292-304. doi:https://
doi.org/10.1016/j.asoc.2015.11.038.
[13] 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, 271-285. doi:https://doi.org/
10.1007/978-3-319-46819-8_18.
[14] 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. doi:https://doi.org/10.1007/s11053-019-09474-9.
[15] 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. doi:https://doi.org/10.3390/min13030370.
[16] Guardiano, E., Parker, H., Isaaks, E. (1995). Prediction of Recoverable Reserves Using Conditional Simulation: A Case Study for the Fort Knox Gold Project, Alaska. Unpublished Technical Report; Mineral Resource Development Inc.: Port Moresby, Papua New Guinea.
[17] Kingston, G. (1977). Reserve classification of identified nonfuel mineral resources by the bureau of mines minerals availability system. Journal of the International Association for Mathematical Geology, 9, 273–279. doi:https://doi.org/
10.1007/BF02272389.
[18] Dimitrakopoulos, R., Chou, C., Godoy, M. (2008). Resource / Reserve Classification with Integrated Geometric and Local Grade Variability Measures. Cosmo 08.
[19] Asghari, O., Esfahani, NM. (2014). Erratum to: A new approach for the geological risk evaluation of coal resources through a geostatistical simulation. Arabian Journal of Geosciences, 7, 839. doi:https://doi.org/10.1007/s12517-013-1262-1.
[20] Peattie, R., Dimitrakopoulos, R. (2013). Forecasting Recoverable Ore Reserves and Their Uncertainty at Morila Gold Deposit, Mali: An Efficient Simulation Approach and Future Grade Control Drilling. Math Geosci, 45, 1005-1020. doi:https://
doi.org/10.1007/s11004-013-9478-x.
[21] Tajvidi, E., Monjezi, M., Asghari, O., Emery, X., Foroughi, S. (2015). Application of joint conditional simulation to uncertainty quantification and resource classification. Arabian Journal of Geosciences, 8, 455-463. doi:https://doi.org/
10.1007/s12517-013-1133-9.
[22] Deustch, C., Leaungthong, O., Ortiz, J. (2007). Case for geometric criteria in resources and reserves classifcation. Trans Soc Min Metall Explor 322.
[23] 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.
[24] Dohm, C. (2005). Quantifiable Mineral Resource Classification: A Logical Approach. Geostatistics Banff 2004, 333-342. doi:https://doi.org/10.1007/978-1-4020-3610-1_34.
[25] 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. doi:https://doi.org/
10.1007/s42461-021-00478-9.
[26] Stephenson, P., Stoker, P. (2001). Mineral resource and ore reserve estimation - the AusIMM guide to good practice (monograph 23). Miner Eng, 14(9). doi:https://doi.org/
10.1016/s0892-6875(01)80033-9.
[27] Owusu, S. (2019). Critical Review of Mineral Resource Classification Techniques in the Gold Mining Industry. Insights in Mining Science & Technology, 1(3), 555564. doi:https://doi.org/10.19080/imst.2019.01.555564.
[28] Machuca-Mory, D., Deutsch, C. (2006). A Program for Robust Calculation of Drillhole Spacing in Three Dimensions.
[29] Delaunay, B. (1934). Sur la sphere vide. Bulletin de l’Académie des Sciences de l’URSS 6.
[30] Wilde, B., Deutsch, C V. (2010). Data spacing and uncertainty: Quantification and complications. IAMG 2010 Budapest - 14th Annual Conference of the International Association for Mathematical Geosciences.
[31] 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. doi:https://doi.org/10.1007/s11004-005-9021-9.
[32] 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.
[33] 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. doi:https://doi.org/
10.1080/12269328.2020.1748524.
[34] 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. doi:https://doi.org/10.1201/9780429320774-14.
[35] Journel, AG. (1983). Nonparametric estimation of spatial distributions. Journal of the International Association for Mathematical Geology, 15, 445-468. doi:https://doi.org/
10.1007/BF01031292.
[36] Jelvez, E., Ortiz, J., Morales, N., Askari, H., Nelis, G. (2023). A Multi-Satage Methodology for Long-Term Open-Pit Mine Production Planning under Ore Grade Uncertainty. Mathematics, 11(18).
[37] Ribeiro, DT., Filho, CGM., de Souza, LE., Costa, JFCL., de Almeida D del PM. (2012). Utilização de critérios geoestatísticos para comparação de malha de sondagem visando à maximização da quantidade de recursos. Revista Escola de Minas, 65(1). doi:https://doi.org/10.1590/S0370-44672012000100016.
[38] Madani, N. (2020). Mineral resource classification based on uncertainty measures in geological domains. Springer Series in Geomechanics and Geoengineering, 157-164. doi:https://doi.org/10.1007/978-3-030-33954-8_19.
[39] Wawruch, TM., Betzhold, JF. (2005). Mineral Resource Classification Through Conditional Simulation. Geostatistics Banff 2004, 479-489. doi:https://doi.org/10.1007/978-1-4020-3610-1_48.
[40] 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). doi:https://doi.org/10.17159/2411-9717/660/2019.
[41] Silva, DSF., Boisvert, JB. (2014). Mineral resource classification: A comparison of new and existing techniques. J South Afr Inst Min Metall 114.
[42] Arik, A. (2002). Comparison of resource classification methodologies with a new approach. 30th International Symposium on the Application of Computers and Operations Research in the Mineral Industry.
[43] Abzalov, M. (2016). Methodology of the mineral resource classification. Modern Approaches in Solid Earth Sciences, 355-363. doi:https://doi.org/10.1007/978-3-319-39264-6_28.
[44] Caers, J. (2011). Modeling Uncertainty in the Earth Sciences. Modeling Uncertainty in the Earth Sciences. doi:https://doi.org/10.1002/9781119995920.
[45] Pyrcz, M., Deutsch, C. (2014). Geostatistical Reservoir Modeling (2nd Edition). Oxford University Press.
[46] 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
[47] Zuo, M., Wang, T. (2021). Research on reserve classification of solid mineral resources in China and western countries. IOP Conf Ser Earth Environ Sci, 631. doi:https://doi.org/10.1088/1755-1315/631/1/012044.
[48] Duggan, S., Grills, A., Stiefenhofer, J., Thurston, M. (2017). Development of a best-practice mineral resource classification system for the de Beers group of companies. J South Afr Inst Min Metall, 117(12). doi:https://doi.org/10.17159/2411-9717/2017/v117n12a6.
[49] Mohanlal, K., Stevenson, P. (2010). Anglo American Platinum’s approach to resource classification case study—Boschkoppie/Styldrift minewide UG2 project. The 4th International Platinum Conference, Platinum in Transition ‘Boom or Bust.
[50] Rocha V, A., 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. doi:https://doi.org/10.1080/25726838.2023.2244775.
[51] Ortiz, J., Deutsch, C. (2003). A practical way to summarize uncertainty for classifcation. Centre for computational geostatistics, report fve, University of Alberta 14.
[52] Glacken, I., Snowden, D. (2001). Mineral resource estimation, In Edwards, A. C.
[53] Revuelta, MB. (2018). Mineral Resources :From Exploration to Sustainability Assessment.
[54] Da Rocha, MM., Yamamoto, JK. (2000). Comparison between kriging variance and interpolation variance as uncertainty measurements in the Capanema iron mine, State of Minas Gerais-Brazil. Natural Resources Research, 9, 223-235. doi:https://doi.org/10.1023/a:1010195701968.
[55] Rossi, ME., Deutsch, C V. (2014). Mineral Resource Estimation. doi:https://doi.org/10.1007/978-1-4020-5717-5.
[56] Emery, X. (2008). Uncertainty modeling and spatial prediction by multi-Gaussian kriging: Accounting for an unknown mean value. Comput Geosci, 34(11), 1431-1442. doi:https://doi.org/
10.1016/j.cageo.2007.12.011.
[57] McManus, S., Rahman, A., Horta, A., Coombes, J. (2020). Applied Bayesian Modeling for Assessment of Interpretation Uncertainty in Spatial Domains. Statistics for Data Science and Policy Analysis, 3-13. doi:https://doi.org/10.1007/978-981-15-1735-8_1.
[58] Riquelme, ÁI., Ortiz, JM. (2021). Uncertainty Assessment over any Volume without Simulation: Revisiting Multi-Gaussian Kriging. Math Geosci, 53, 1375-1405. doi:https://doi.org/10.1007/s11004-020-09907-9.
[59] Fouedjio, F., Klump, J. (2019). Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches. Environ Earth Sci, 78(38). doi:https://doi.org/10.1007/s12665-018-8032-z.
[60] Mery, N., Marcotte, D. (2022). Assessment of Recoverable Resource Uncertainty in Multivariate Deposits Through a Simple Machine Learning Technique Trained Using Geostatistical Simulations. Natural Resources Research, 31, 767-783. doi:https://doi.org/10.1007/s11053-022-10028-9.
[61] Lindi, OT., Aladejare, AE., Ozoji, TM., Ranta, J-P. (2024). Uncertainty Quantification in Mineral Resource Estimation. Natural Resources Research, 33, 2503–2526.
[62] Mery, N., Emery, X., Cáceres, A., Ribeiro, D., Cunha, E. (2017). Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geol Rev, 88, 336-351. doi:https://
doi.org/10.1016/j.oregeorev.2017.05.011.
[63] 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.
[64] Dumakor-Dupey, NK., Arya, S. (2021). Machine learning—a review of applications in mineral resource estimation. Energies (Basel). doi:https://doi.org/10.3390/en14144079.
[65] Solomatine, DP., Shrestha, DL. (2009). A novel method to estimate model uncertainty using machine learning techniques. Water Resour Res, 45(12). doi:https://doi.org/
10.1029/2008WR006839.
[66] Li, T., Xia, Q., Ouyang, Y., Zeng, R., Liu, Q., Li, T. (2024). Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms. Natural Resources Research, 33, 1049–1071.
[67] Zhao, J., Chi, H., Shao, Y., Peng, X. (2022). Application of AdaBoost Algorithms in Fe Mineral Prospectivity Prediction: A Case Study in Hongyuntan–Chilongfeng Mineral District, Xinjiang Province, China. Natural Resources Research, 31, 2001–2022.
[68] Farhadi, S., Tatullo, S., Boveiri Konari, M., Afzal, P. (2024). Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping. Journal of Geochemical Exploration, 260, 107441. doi: https://doi.org/10.1016/j.gexplo.2024.107441.
[69] Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., Sadeghi, B. (2022). Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals, 12(6), 689. doi: https://doi.org/10.3390/min12060689.
[70] Cotrina, M.A., Marquina, J.J., Riquelme, A.I. (2025). Comparison of Machine Learning Techniques for Mineral Resource Categorization in a Copper Deposit in Peru. Natural Resources Research. doi: https://doi.org/10.1007/s11053-025-10505-x.
[71] Desai, C. (2020). Comparative Analysis of Optimizers in Deep Neural Networks. Int J Innov Sci Res Technol 5.
[72] Hassan, E., Shams, MY., Hikal, NA., Elmougy, S. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed Tools Appl, 82, 16591-16633. doi:https://doi.org/10.1007/s11042-022-13820-0.
[73] Nanni, L., Maguolo, G., Lumini, A. (2021). Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks. Computer Science. doi:https://doi.org/https://doi.org/10.48550/arXiv.2103.14689.
[74] Hernández, H., Alberdi, E., Goti, A., Oyarbide-Zubillaga, A. (2023). Application of the k-Prototype Clustering Approach for the Definition of Geostatistical Estimation Domains. Mathematics, 11(3), 740. doi:https://doi.org/
10.3390/math11030740.
[75] Bianchi, M., Zheng, C. (2009). SGeMS: A free and versatile tool for three-dimensional geostatistical applications. Ground Water. doi:https://doi.org/10.1111/j.1745-6584.2008.00522.x.
[76] Remy, N. (2005). S-GeMS: The Stanford Geostatistical Modeling Software: A Tool for New Algorithms Development. doi:https://doi.org/10.1007/978-1-4020-3610-1_89.
[77] Ali, Rezaei., Hossein, Hassani., Parviz, Moarefvand., Abbas, Golmohammadi. (2019) Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran. Global Journal of Earth Science and Engineering, 6(2019). doi:https://doi.org/10.15377/2409-5710.2019.06.4.
[78] Heuvelink, GBM., Pebesma, EJ. (2002). Is The Ordinary Kriging Variance A Proper Measure Of Interpolation Error? The fifth international symposium on spatial accuracy assessment in natural resources and environmental sciences.
[79] da Silva, CZ., Nisenson, J., Boisvert, J. (2022). Grade Control with Ensembled Machine Learning: A Comparative Case Study at the Carmen de Andacollo Copper Mine. Natural Resources Research, 31, 785-800. doi:https://doi.org/10.1007/s11053-022-10029-8.
[80] Tülay., BAYRAMİN, T. (2016). Assessment of ınverse distance weighting (ıdw) ınterpolation on spatial variability of selected soil properties in the Cukurova plain. Tarım Bilimleri Dergisi. doi:https://doi.org/10.1501/tarimbil_0000001396.
[81] Estrada-Gil, JK., Fernández-López, JC., Hernández-Lemus, E., Silva-Zolezzi, I., Hidalgo-Miranda, A., Jiménez-Sánchez, G., Vallejo-Clemente, EE. (2007). GPDTI: A genetic programming decision tree induction method to find epistatic effects in common complex diseases. Bioinformatics. doi:https://doi.org/
10.1093/bioinformatics/btm205.
[82] Marinos, V., Marinos, P., Hoek, E. (2005). The geological strength index: Applications and limitations. Bulletin of Engineering Geology and the Environment. doi:https://doi.org/10.1007/s10064-004-0270-5.
[83] Emery, X. (2009). The kriging update equations and their application to the selection of neighboring data. Comput Geosci, 13, 269-280. doi:https://doi.org/10.1007/s10596-008-9116-8.
[84] Adhikary, SK., Muttil, N., Yilmaz, AG. (2016). Genetic Programming-Based Ordinary Kriging for Spatial Interpolation of Rainfall. J Hydrol Eng, 21(2). doi:https://doi.org/
10.1061/(asce)he.1943-5584.0001300.
[85] 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, 11,1207–1218.
[86] Dorman, KS., Maitra, R. (2022). An efficient k-modes algorithm for clustering categorical datasets. Stat Anal Data Min. doi:https://doi.org/10.1002/sam.11546.
[87] 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, 15,1011–1027.
[88] Cotrina, M., Marquina, J., Mamani, J., Arango, S., Gonzalez, J., Ccatamayo, J., Noriega E. (2024). Predictive model using machine learning to determine fuel consumption in CAT-777F mining equipment. Int J Min Miner Eng, 15, 147–160.
[89] 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, 1345–1355.
[90] Joseph, FJJ., Nonsiri, S., Monsakul, A. (2021). Keras and TensorFlow: A Hands-On Experience. EAI/Springer Innovations in Communication and Computing. doi:https://doi.org/10.1007/978-3-030-66519-7_4.
[91] Kingma, DP., Ba, JL. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.
[92] Elshamy, R., Abu-Elnasr, O., Elhoseny, M., Elmougy, S. (2023). Improving the efficiency of RMSProp optimizer by utilizing Nestrove in deep learning. Sci Rep, 13, 8814.
[93] Tian, Y., Zhang, Y., Zhang, H. (2023). Recent Advances in Stochastic Gradient Descent in Deep Learning. Mathematics, 11, 682.
[94] Lydia, AA., Francis, FS. (2019). Adagrad - An Optimizer for Stochastic Gradient Descent. INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE 6.
[95] Yacouby, R., Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. doi:https://doi.org/
10.18653/v1/2020.eval4nlp-1.9.
[96] Dalianis, H. (2018). Evaluation Metrics and Evaluation. Clinical Text Mining. doi:https://doi.org/10.1007/978-3-319-78503-5_6.