Conference Proceedings - World Mining Congress 2026

161 Guizado Jol, J. (1968). Geología del cuadrángulo de Aplao 33-r (Boletín A 20). Instituto Geológico Minero y Metalúrgico (INGEMMET). Holland, S. M. (2008). Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA, 30602, 2501. 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), 1021. Lauzon, D., & Gloaguen, E. (2024). Quantifying uncertainty and improving prospectivity mapping in mineral belts using transfer learning and random forest: A case study of copper mineralization in the Superior Craton Province, Quebec, Canada. Ore Geology Reviews, 166, 105918. Lou, Y., & Liu, Y. (2023). Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China. Earth and Space Science, 10(10), e2022EA002596. Mantilla-Dulcey, A., Goyes-Peñafiel, P., Báez-Rodríguez, R., & Khurama, S. (2024). Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning. Ore Geology Reviews, 169, 106458. Mitra, S., Mallick, S., Biswas, S., & Patra, K. (2025). Advanced machine learning-based gold prospectivity mapping in the Dharwar Craton, India: A hybrid knowledge-data driven paradigm integrating ensemble and deep learning. Ore Geology Reviews, 172, 107012. Origel-Gutiérrez, G., & Cárdenas, E. G. S. (2010). Guía metodológica para la detección de áreas de alteración minera con imágenes ASTER. In XIV Simposio de la Sociedad Latinoamericana de Teledetección. Palomares, F. G., Monsoriu, J. A., & Alemany, E. (2016). Aplicación de la convolución de matrices al filtrado de imágenes. Modelling in Science Education and Learning, 9(1), 97-108. Pecho Gutiérrez, V., & Morales Serrano, L. G. (1969). Geología de los cuadrángulos de Camaná y La Yesera (Boletín A 21). Instituto Geológico Minero y Metalúrgico (INGEMMET). Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 7, e6876. https://doi.org/10.7717/peerj.6876 Shaw, K. O., Goïta, K., & Germain, M. (2022). Prospectivity mapping of heavy mineral ore deposits based upon machine-learning algorithms: Columbite-tantalite deposits in West-Central Côte d’Ivoire. Minerals, 12(11), 1453. Taherdoost, H., Sahibuddin, S., & Jalaliyoon, N. (2014). Exploratory factor analysis: Concepts and theory. Advances in Applied and Pure Mathematics, 27, 375–382. Yan, Q., Pei, Y., Xue, L., Sun, H., Wang, R., & Ran, X. (2025). Mineral prospectivity mapping: An interpretable classifier combining catchment basin and knowledge graph embedding. Ore Geology Reviews, 106758.

RkJQdWJsaXNoZXIy MTM0Mzk2