Referencias seleccionadas

Listado abreviado. La tesis completa cita el conjunto en el PDF (págs. 1–151).

Abdi, H., & Williams, L. J. (2010). Principal component analysis. WIREs Computational Statistics, 2(4), 433–459.

Berger, B. R., Ayuso, R. A., Wynn, J. C., & Seal, R. R. (2008). Preliminary model of porphyry copper deposits (USGS Open-File Report 2008-1321).

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.

Carranza, E. J. M., & Laborte, A. G. (2015). Data-driven predictive mapping of mineral prospectivity using Random Forest and k-nearest neighbor algorithms. Natural Resources Research, 24(2), 137–152.

Carrillo, J., et al. (2019). Modelamiento de variables geometalúrgicas basado en ciencia de datos [Tesis de título profesional]. Universidad Nacional de Ingeniería.

Cate, A., et al. (2018a). Machine learning as a tool for geologists. Geochemistry: Exploration, Environment, Analysis, 18(4), 411–424.

Cate, A., et al. (2018b). Classification of lithostratigraphic and alteration units from drillhole lithogeochemical data using machine learning: Lalor VMS deposit. Journal of Geochemical Exploration, 188, 321–339.

Corbett, G. J., & Leach, T. M. (1998). Southwest Pacific Rim gold-copper systems (SEG Special Publication 6).

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.

Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms. Computers & Geosciences.

Deyell, C. L., et al. (2005). Alunite in the Pascua-Lama high-sulfidation system. Economic Geology.

Dramsch, J. S. (2020). 70 years of machine learning in geoscience in review. Advances in Geophysics.

Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis. Wiley.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hedenquist, J. W., & Lowenstern, J. B. (1994). The role of magmas in the formation of hydrothermal ore deposits. Nature.

Hill, E. J., et al. (2014). Using geochemical proxies to model nuggety gold deposits: Sunrise Dam.

Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys.

Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Phil. Trans. R. Soc. A.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.

Lowell, J. D., & Guilbert, J. M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic Geology.

Mallma Espinoza, J. P. (2026). Delimitación de los dominios geológicos de alteración desde firmas espectrales y geoquímica mediante el empleo de machine learning [Tesis de pregrado]. Universidad Nacional de Ingeniería.

Pour, A. B., & Hashim, M. (2014). Hydrothermal alteration mapping from Landsat-8 data. Advances in Space Research.

Rodríguez-Galiano, V. F., et al. (2015). Machine learning predictive models for mineral prospectivity. Ore Geology Reviews.

Rusk, B. G., Reed, M. H., & Dilles, J. H. (2008). Fluid inclusion evidence for magmatic-hydrothermal fluid evolution. Economic Geology.

Sillitoe, R. H. (1993). Epithermal models: Genetic types, geometrical controls and shallow features.

Sillitoe, R. H. (2010). Porphyry copper systems. Economic Geology, 105, 3–41.

Titley, S. R., & Beane, R. E. (1981). Porphyry copper deposits. Economic Geology 75th Anniversary Volume.

White, N. C., et al. (1995). High-sulfidation epithermal deposits.