Document Type : Original Research Paper
Authors
- Marco Antonio Cotrina-Teatino 1
- Jairo Jhonatan Marquina-Araujo 1
- Jose Nestor Mamani-Quispe 2
- Jorge Chira-Fernandez 3
- Cesar De la cruz-Poma 3
- Solio Marino Arango-Retamozo 1
1 Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru
2 Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru
3 Dirección de Recursos Minerales y Energéticos, INGEMMET, Lima, Peru
10.22044/jme.2025.16468.3212
Abstract
The sustained increase in mining waste, particularly in the form of tailings, poses a significant environmental and economic challenge, especially in contexts where these deposits retain residual metal content. This study assessed the gold potential of Tailings Deposit I at La Cienega (Peru) by integrating geostatistical estimation and machine learning models optimized through metaheuristic algorithms. The methodology involved geochemical characterization, three-dimensional estimation using Ordinary Kriging (OK) as a geostatistical method, and prediction of gold grades through three models: XGBoost optimized with Particle Swarm Optimization (XGB+PSO), Support Vector Regression with Genetic Algorithm (SVR+GA), and Random Forest optimized using Ant Colony Optimization (RF+ACO). Estimates were validated using Leave-One-Out cross-validation and performance metrics including RMSE, MAE, Bias, and correlation coefficient (R). The RF+ACO model achieved an RMSE of 0.32 ppm, MAE of 0.24 ppm, Bias of 0.006, and an R value of 0.56. Average predicted grades ranged from 1.14 to 1.33 ppm, with estimated gold contents between 981.00 and 1,147.12 ounces, while OK yielded 1,028.77 ounces at an average grade of 1.19 ppm. These findings suggest that properly optimized machine learning models can provide reasonable estimates of metal content in tailings, particularly in settings characterized by high spatial heterogeneity and limited geological continuity.
Keywords
Main Subjects
[1]. Hudson-Edwards, K. A., Jamieson, H. E., & Lottermoser, B. G. (2011). Mine wastes: past, present, future. Elements, 7(6), 375-380.
[2]. Abbadi, A., & Mucsi, G. (2024). A review on complex utilization of mine tailings: recovery of rare earth elements and residue valorization. Journal of Environmental Chemical Engineering, 12(3), 1-18.
[3]. Edraki, M., Baumgartl, T., Manlapig, E., Bradshaw, D., Franks, D. M., & Moran, C. J. (2014). Designing mine tailings for better environmental, social and economic outcomes: a review of alternative approaches. Journal of Cleaner Production, 84, 411-420.
[4]. Falagán, C., Grail, B. M., & Johnson, D. B. (2017). New approaches for extracting and recovering metals from mine tailings. Minerals Engineering, 106, 71-78.
[5]. Farhadi, S., Afzal, P., Konari, M. B., Saein, L. D., & 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.
[6]. Cotrina-Teatino, M. A., Marquina-Araujo, J. J., Mamani-Quispe, J. N., Chira-Fernandez, J. E., Castillo-Chung, A. R., Arango-Retamozo, S. M., González-Vasquez, J. A., & Ortiz-Quintanilla, S. M. (2025). Geochemical and mineralogical characterization of critical elements in gold tailings from the La Cienega, Peru, and assessment of their reuse potential. Journal of Environmental Chemical Engineering, 13(5), 118497.
[7]. Lemos, M., Valente, T., Marinho Reis, P., Fonseca, R., Delbem, I., Ventura, J., & Magalhães, M. (2021). Mineralogical and geochemical characterization of gold mining tailings and their potential to generate acid mine drainage (Minas Gerais, Brazil). Minerals, 11(1), 39.
[8]. Lottermoser, B. (2010). Mine wastes Characterization, Treatment and Environmental Impacts. Earth and Environmental Science.
[9]. Lemos, M. G., Valente, T., Marinho-Reis, A. P., Fonsceca, R., Dumont, J. M., Ferreira, G. M. M., & Delbem, I. D. (2021). Geoenvironmental study of gold mining tailings in a circular economy context: Santa Barbara, Minas Gerais, Brazil. Mine Water and the Environment, 40, 257-269.
[10]. Parviainen, A., Kauppila, T., & Loukola-Ruskeeniemi, K. (2012). Long-term lake sediment records and factors affecting the evolution of metal(loid) drainage from two mine sites (SW Finland). Journal of Geochemical Exploration, 114, 46-56.
[11]. González-Díaz, E., García, S., Soto, F., Navarro, F., Townley, B., & Caraballo, M. A. (2022). Geochemical, mineralogical and geostatistical modelling of an IOCG tailings deposit (El Buitre, Chile): implications for environmental safety and economic potential. Journal of Geochemical Exploration, 239, 106997.
[12]. Kossoff, D., Dubbin, W. E., Alfredsson, M., Edwards, S. J., Macklin, M. G., & Hudson-Edwards, K. A. (2014). Mine tailings dams: characteristics, failure, environmental impacts, and remediation. Applied Geochemistry, 51, 229-245.
[13]. Lyu, Z., Chai, J., Xu, Z., Qin, Y., & Cao, J. (2019). A comprehensive review on reasons for tailings dam failures based on case history. Advances in Civil Engineering, 2019(1), 1-18.
[14]. Yin, Z., Sun, W., Hu, Y., Zhang, C., Guan, Q., & Wu, K. (2018). Evaluation of the possibility of copper recovery from tailings by flotation through bench-scale, commissioning, and industrial tests. Journal of Cleaner Production, 171, 1039-1048.
[15]. Araya, N., Ramírez, Y., Cisternas, L. A., & Kraslawski, A. (2021). Use of real options to enhance water–energy nexus in mine tailings management. Appl Energy, 303, 117626.
[16]. Cotrina-Teatino, M. A., & Marquina-Araujo, J. J. (2025). Circular economy in the mining industry: a bibliometric and systematic literature review. Resources Policy, 102, 105513.
[17]. Shaforostova, E. N., Kosareva-Volod’ko, O. V., Belyankina, O. V., Solovykh, D. Y., Sazankova, E. S., Sizova, E. I., & Adigamov, D. A. (2023). A tailing dump as industrial deposit: study of the mineralogical composition of tailing dump of the Southern Urals and the possibility of tailings re-development. Resources, 12(2), 28.
[18]. Manaviparast, H. R., Miranda, T., Pereira, E., & Cristelo, N. (2024). A comprehensive review on mine tailings as a raw material in the alkali activation process. Applied Sciences, 14(12), 5127.
[19]. Pizarro Barraza, F., Thiyagarajan, D., Ramadoss, A., Manikandan, V. S., Dhanabalan, S. S., Abarzúa, C. V., Sotomayor Soloaga, P., Campos Nazer, J., Morel, M. J., & Thirumurugan, A. (2024). Unlocking the potential: mining tailings as a source of sustainable nanomaterials. Renewable and Sustainable Energy Reviews, 202, 114665.
[20]. Kinnunen, P., Ismailov, A., Solismaa, S., Sreenivasan, H., Räisänen, M. L., Levänen, E., & Illikainen, M. (2018). Recycling mine tailings in chemically bonded ceramics: a review. Journal of Cleaner Production, 174, 634-649.
[21]. Solismaa, S., Ismailov, A., Karhu, M., Sreenivasan, H., Lehtonen, M., Kinnunen, P., Illikainen, M., & Räisänen, M. L. (2018). Valorization of Finnish mining tailings for use in the ceramics industry. Bulletin of the Geological Society of Finland, 90, 33-54.
[22]. Spooren, J., Binnemans, K., Björkmalm, J., et al. (2020). Near-zero-waste processing of low-grade, complex primary ores and secondary raw materials in Europe: technology development trends. Resources, Conservation and Recycling, 160, 104919.
[23]. Vitti, C., & Arnold, B. J. (2022). The reprocessing and revalorization of critical minerals in mine tailings. Mining, Metallurgy & Exploration, 39, 49-54.
[24]. Cotrina-Teatino, M. A., Marquina-Araujo, J. J., Avalos-Murga, J. A., & Carrion-Villacorta, F. L. (2025). Flotation of mine tailings: a bibliometric analysis and systematic literature review. Journal of Environmental Chemical Engineering, 13(2), 116136.
[25]. Blannin, R., Frenzel, M., Tolosana-Delgado, R., Büttner, P., & Gutzmer, J. (2023). 3D geostatistical modelling of a tailings storage facility: resource potential and environmental implications. Ore Geology Reviews, 154, 105337.
[26]. Leskelä, T., Hokka, J., & Karlsson, T. (2025). Geostatistical modelling of mine tailings and comparative analysis of sampling methodologies: a case study of the Otanmäki ilmenite tailings storage facility project. Journal of Geochemical Exploration, 274, 107759.
[27]. Expósito-Suárez, V. M., Suárez-Navarro, J. A., Caro, A., et al. (2024). Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the co-kriging method. Environmental Geochemistry and Health, 46, 297.
[28]. Parviainen, A., Soto, F., & Caraballo, M. A. (2020). Revalorization of Haveri Au–Cu mine tailings (SW Finland) for potential reprocessing. Journal of Geochemical Exploration, 218, 106614.
[29]. Soto, F., Navarro, F., Díaz, G., Emery, X., Parviainen, A., & Egaña, Á. (2022). Transitive kriging for modeling tailings deposits: a case study in southwest Finland. Journal of Cleaner Production, 374, 133857.
[30]. Cotrina-Teatino, M. A., & Marquina-Araujo, J. J. (2025). Geostatistical models, recovery, revalorization and economic potential in mine tailings: a comprehensive bibliometric and systematic review. Journal of Cleaner Production, 509, 145617
[31]. Madani, N., Maleki, M., & Parviainen, A. (2025). Enhancing multivariate geostatistical simulation in mine tailings using the projection pursuit multivariate transform and coregionalization analysis. Mathematical Geosciences.
[32]. García, S., Camus, L., Gonzalez-Diaz, E., Collao, R., Townley, B., Parviainen, A., & Caraballo, M. A. (2024). The importance of geochemical and mineralogical characterization of fresh Cu-porphyry mine tailings in mineral processing plants to optimize their revalorization potential. Journal of Geochemical Exploration, 259, 107439.
[33]. Nascimento, S. C., Cooke, D. R., Cracknell, M. J., Miller, C. B., & Parbhakar-Fox, A. (2025). Mineralogical and geochemical characterization of mine tailings in the King River Delta, Western Tasmania: implications for long-term stability of trace elements. Applied Geochemistry, 184, 106366
[34]. Redwan, M., & Bamousa, A. O. (2019). Characterization and environmental impact assessment of gold mine tailings in arid regions: a case study of Barramiya gold mine area, Eastern Desert, Egypt. Journal of African Earth Sciences, 160, 103644.
[35]. Samanta, B., Bandopadhyay, S., & Ganguli, R. (2002). Data segmentation and genetic algorithms for sparse data division in Nome placer gold grade estimation using neural network and geostatistics. Exploration and Mining Geology, 11(1–4), 69–76
[36]. Kaplan, U. E., & Topal, E. (2020). A new ore grade estimation using combine machine learning algorithms. Minerals, 10(10), 847.
[37]. Marquina-Araujo, J. J., Cotrina-Teatino, M. A., Mamani-Quispe, J. N., Noriega-Vidal, E. M., Vega-Gonzalez, J. A., & Cruz-Galvez, J. (2024). Copper ore grade prediction using machine learning techniques in a copper deposit. Journal of Mining and Environment, 15(3), 1011–1027.
[38]. 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.
[39]. Dey, T., & Samanta, G. (2025). Grade estimation using machine learning techniques in Indian copper deposits. Journal of The Institution of Engineers (India): Series D, 106(1), 717–723
[40]. Tsae, N. B., Adachi, T., & Kawamura, Y. (2023). Application of artificial neural network for the prediction of copper ore grade. Minerals, 13(5), 658.
[41]. Bazdar, H., Fattahi, H., & Ghadimi, F. (2015). Hybrid ANN with invasive weed optimization algorithm, a new technique for prediction of gold and silver in Zarshuran gold deposit, Iran. Journal of Tethys, 3(3), 273–285
[42]. Yu, S., Zhu, K., & He, Y. (2012). A hybrid intelligent optimization method for multiple metal grades optimization. Neural Computing and Applications, 21(6), 1391–1402
[43]. Jafrasteh, B., Fathianpour, N., & Suárez, A. (2018). Comparison of machine learning methods for copper ore grade estimation. Computational Geosciences, 22, 1371–1388.
[44]. 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
[45]. 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.
[46]. 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.
[47]. Pourgholam, M. M., Adib, A., Afzal, P., Rahbar, K., & Gholinejad, M. (2025). Deep learning and fractal-wavelet techniques for magnetite-apatite exploration in Tarom, Iran. Scientific Reports, 15(1), 31907.
[48]. Cotrina-Teatino, M. A., Marquina-Araujo, J. J., Mamani-Quispe, J. N., Guartán, J. A., Castillo-Chung, A. R., Arango-Retamozo, S. M., González-Vasquez, J. A., & Ortiz-Quintanilla, S. M. (2025). Strategic potential assessment of lanthanum and scandium through geochemical–lithological analysis with unsupervised machine learning in southern Ecuador. Resources Policy, 109, 105731.
[49]. Manrique, M., & Agurto, A. (2022). Caracterización geoquímica y mineralógica de relaves mineros La Ciénaga – La Libertad. INGEMMET, Boletín, Serie B: Geología Económica, 78, 143.
[50]. Walsh Perú. (2017). El Entorno Social: Estudio de caso de pasivos ambientales mineros en la región La Libertad / Perú. Las Relaveras de La Ciénaga / Pataz. Santiago: Cooperación Alemana para el Desarrollo; Instituto Federal de Geociencias y Recursos Naturales (BGR).
[51]. Meyer, C., Jerez, I., & Soundy, R. (2019). Relaves “La Ciénaga” Pataz en Perú: Informe sobre caso de estudio para la remediación de los relaves en La Ciénaga, Pataz en Perú. UMTEC – Biener / Sasse / Konertz – Partnerschaft Beratender Ingenieure und Geologen mbB, 47.
[52]. Mpinga, C. N., Bradshaw, S. M., Akdogan, G., Snyders, C. A., & Eksteen, J. J. (2014). Evaluation of the Merrill–Crowe process for the simultaneous removal of platinum, palladium and gold from cyanide leach solutions. Hydrometallurgy, 142, 36–46.
[53]. Haeberlin, Y. (2002). Geological and structural setting, age, and geochemistry of the orogenic gold deposits at the Pataz Province, Eastern Andean Cordillera, Peru. Terre et Environnement, 36, 182.
[54]. Schreiber, D. W., Fontboté, L., & Lochmann, D. (1990). Geologic setting, paragenesis, and physicochemistry of gold quartz veins hosted by plutonic rocks in the Pataz region. Economic Geology, 85(7), 1328–1347.
[55]. Witt, W. K., Hagemann, S. G., Villanes, C., Vennemann, T., Zwingmann, H., Laukamp, C., & Spangenberg, J. E. (2016). Multiple gold mineralizing styles in the Northern Pataz District, Peru. Economic Geology, 111(2), 355–394.
[56]. Mendoza, A. (2016). Estudio de potencial neto de neutralización de dos muestras de relaves. Informe técnico, abril 2016. Investigación Metalúrgica No. 18148, BGR Chile.
[57]. Morche, W. (2017). Estudio de caso de pasivos ambientales mineros en la región La Libertad/Perú: Las relaveras de La Ciénaga/Pataz. Investigación económico-minera y ambiental. Cooperación Alemana para el Desarrollo – Instituto Federal de Geociencias y Recursos Naturales (BGR), Trujillo (Perú) – Santiago (Chile).
[58]. Oré, G. (2017). Gisement de Poderosa (Pérou): une minéralisation aurifère filonienne de filiation magmatique. Thèse de doctorat, Université du Québec à Chicoutimi, Québec, Canada.
[59]. Voute, F., Hagemann, S. G., Evans, N. J., & Villanes, C. (2019). Sulfur isotopes, trace element, and textural analyses of pyrite, arsenopyrite and base metal sulfides associated with gold mineralization in the Pataz-Parcoy district, Peru: Implication for paragenesis, fluid source, and gold deposition mechanisms. Mineralium Deposita, 54(7), 1077–1100.
[60]. Pesquer, L., Cortés, A., & Pons, X. (2011). Parallel ordinary kriging interpolation incorporating automatic variogram fitting. Computers & Geosciences, 37(4), 464–473.
[61]. Ramadhan, M. D., Marwanza, I., Nas, C., Azizi, M. A., Dahani, W., & Kurniawati, R. (2021). Drill holes spacing analysis for estimation and classification of coal resources based on variogram and kriging. IOP Conference Series: Earth and Environmental Science, 819, 012026.
[62]. Deutsch, C. V. (1996). Correcting for negative weights in ordinary kriging. Computers & Geosciences, 22(7), 765–773.
[63]. Wackernagel, H. (2003). Ordinary kriging. In Multivariate geostatistics: An introduction with applications (3rd ed., pp. 79–88). Springer, Berlin, Heidelberg.
[64]. Cotrina-Teatino, M. A., Marquina-Araujo, J. J., & Riquelme, Á. I. (2025). Comparison of machine learning techniques for mineral resource categorization in a copper deposit in Peru. Natural Resources Research, 34(4), 2007–2025.
[65]. Xiong, J., Gong, Y., Liu, X., Li, Y., Chen, L., Liao, C., & Zhang, C. (2025). A PSO-XGBoost model for predicting the compressive strength of cement–soil mixing pile considering field environment simulation. Buildings, 15(15), 2740.
[66]. Gu, Y., Zhang, D., & Bao, Z. (2021). A new data-driven predictor, PSO-XGBoost, used for permeability of tight sandstone reservoirs: A case study of member of Chang 4+5, western Jiyuan Oilfield, Ordos Basin. Journal of Petroleum Science and Engineering, 199, 108350.
[67]. Ngoc, L. T., Nguyen, H., Bui, X.-N., & Tran, T.-T. (2025). Dynamic weighting and genetic algorithm-based hybrid SVR–Ridge model for improving PM2.5 forecasting in open-pit copper mines. International Journal of Mining, Reclamation and Environment, 1–31.
[68]. Zhang, J., Xu, L., Li, J., Yan, Y., & Xu, R. (2022). Parameter acquisition study of mining-induced surface subsidence probability integral method based on RF–AGA–ENN model. Geofluids, 2022, 1865004.
[69]. Parsa, M. (2021). A data augmentation approach to XGBoost-based mineral potential mapping: An example of carbonate-hosted Zn–Pb mineral systems of western Iran. Journal of Geochemical Exploration, 228, 106811.
[70]. Nabavi, Z., Mirzehi, M., Dehghani, H., & Ashtari, P. (2023). A hybrid model for back-break prediction using XGBoost machine learning and metaheuristic algorithms in Chadormalu iron mine. Journal of Mining and Environment, 14(2), 689–712.
[71]. 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(4), 1049–1071.
[72]. Cotrina, M., Marquina, J., & Polo, J. (2025). Prediction of unit haulage cost in an underground mine using machine learning techniques. Journal of Sustainable Mining, 24(2), 250–266.
[73]. Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30(4), 1015–1024.
[74]. Liu, W., & Zhang, H. (2016). PSO algorithm for block sequencing problem in open pit mining. In Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering, 434–441.
[75]. Goswami, A. D., Mishra, M. K., & Patra, D. (2022). Evaluation of machine learning algorithms for grade estimation using GRNN & SVR. Engineering Research Express, 4(3), 035037.
[76]. Cotrina, M., Marquina, J., Mamani, J., Arango, S., Gonzalez, J., Ccatamayo, J., & Noriega, E. (2025). Hybrid machine learning techniques to predict fuel consumption of dump trucks in an open-pit mine in Peru. International Journal of Mining and Mineral Engineering, 16(1), 1–20.
[77]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
[78]. Acorn, T., Boisvert, J. B., & Leuangthong, O. (2020). Managing geologic uncertainty in pit shell optimization using a heuristic algorithm and stochastic dominance. Mining, Metallurgy & Exploration, 37(2), 375–386.
[79]. Cristianini, N. (2004). Cross-validation (K-fold cross-validation, leave-one-out, jackknife, bootstrap). In Dictionary of Bioinformatics and Computational Biology. John Wiley & Sons.
[80]. Bayat, P., Monjezi, M., Rezakhah, M., & Armaghani, D. J. (2020). Artificial neural network and firefly algorithm for estimation and minimization of ground vibration induced by blasting in a mine. Natural Resources Research, 29(6), 4121–4132.
[81]. Cotrina, M., Marquina, J., Mamani, J., Ccatamayo, J., Gonzalez, J., Arango, S., & Noriega, E. (2024). Predictive model using machine learning to determine fuel consumption in CAT-777F mining equipment. International Journal of Mining and Mineral Engineering, 15(2), 147–160.
[82]. 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.
)