Bergen, K.J., Johnson, P.A., de Hoop, M. V., Beroza, G.C., 2019. Machine learning for data-driven discovery in solid Earth geoscience. Science (80-. ). 363, eaau0323. https://doi.org/10.1126/science.aau0323
Borges, H.P., Aguiar, M.S., 2019. Mineral Classification Using Machine Learning and Images of Microscopic Rock Thin Section, in: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (Eds.), Advances in Soft Computing. Springer, pp. 63–76. https://doi.org/10.1007/978-3-030-33749-0_6
Brandmeier, M., Cabrera Zamora, I.G., Nykänen, V., Middleton, M., 2020. Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox. Nat. Resour. Res. 29, 71–88. https://doi.org/10.1007/s11053-019-09483-8
Breiman, L., 2001. Random forests. Mach. Learn. 56, 5–32.
Carranza, E.J.M., Laborte, A.G., 2016. Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines). Nat. Resour. Res. 25, 35–50. https://doi.org/10.1007/s11053-015-9268-x
Carranza, E.J.M., Laborte, A.G., 2015. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geol. Rev. 71, 777–787. https://doi.org/10.1016/j.oregeorev.2014.08.010
Chawla, N. V, Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 16, 321–357. https://doi.org/10.1613/jair.953
Costa, I., Tavares, F., Oliveira, J., 2019. Predictive lithological mapping through machine learning methods: a case study in the Cinzento Lineament, Carajás Province, Brazil. J. Geol. Surv. Brazil 2, 26–36. https://doi.org/10.29396/jgsb.2019.v2.n1.3
Cracknell, M., Reading, A., W. McNeill, A., 2014. Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random Forests (TM) and Self-Organising Maps, Australian Journal of Earth Sciences. https://doi.org/10.1080/08120099.2014.858081
Cunha, L.M., Cabral Neto, I., Silveira, F.V., Nannini, F., 2017. Apresentação dos resultados do Projeto Diamante brasil, in: Fomentando o Setor Mineral Brasileiro. Ministério de Minas e Energia, Brasília, DF, p. 25.
Deer, W.A., Howie, R.A., Zussman, J., 2013. An Introduction to the Rock-Forming Minerals, 3rd ed. Mineralogical Society of Great Britain and Ireland, London. https://doi.org/10.1180/DHZ
Dramsch, J.S., 2020. 70 years of machine learning in geoscience in review, in: Advances in Geophysics. pp. 1–55. https://doi.org/10.1016/bs.agph.2020.08.002
Ester, M., Kriegel, H.-P., Sander, J., Xiaowei, X., 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in: International Conference on Knowledge Discovery and Data Mining. KDD-96, Portland, Oregon, pp. 226–231. https://doi.org/10.1016/B978-044452701-1.00067-3
Ford, A., 2019. Practical Implementation of Random Forest-Based Mineral Potential Mapping for Porphyry Cu – Au Mineralization in the Eastern Lachlan Orogen , NSW , Australia. Nat. Resour. Res. https://doi.org/10.1007/s11053-019-09598-y
Gavish, Y., O’Connell, J., Marsh, C.J., Tarantino, C., Blonda, P., Tomaselli, V., Kunin, W.E., 2018. Comparing the performance of flat and hierarchical Habitat/Land-Cover classification models in a NATURA 2000 site. ISPRS J. Photogramm. Remote Sens. 136, 1–12. https://doi.org/10.1016/j.isprsjprs.2017.12.002
Grinberg, M., 2018. Flask web development: developing web applications witg python.
Hariharan, S., Tirodkar, S., Porwal, A., Bhattacharya, A., Joly, A., 2017. Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia. Nat. Resour. Res. 26. https://doi.org/10.1007/s11053-017-9335-6
Harris, J.R., Grunsky, E., Behnia, P., Corrigan, D., 2015. Data- and knowledge-driven mineral prospectivity maps for Canada’s North. Ore Geol. Rev. 71, 788–803. https://doi.org/10.1016/j.oregeorev.2015.01.004
Japkowicz, N., Stephen, S., 2002. The class imbalance problem: A systematic study1. Intell. Data Anal. 6, 429–449. https://doi.org/10.3233/IDA-2002-6504
Koch, P.H., Lund, C., Rosenkranz, J., 2019. Automated drill core mineralogical characterization method for texture classification and modal mineralogy estimation for geometallurgy. Miner. Eng. 136, 99–109. https://doi.org/10.1016/j.mineng.2019.03.008
Kuhn, S., Cracknell, M.J., Reading, A.M., 2018. Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia. GEOPHYSICS 83, B183–B193. https://doi.org/10.1190/geo2017-0590.1
Kuhn, S., Cracknell, M.J., Reading, A.M., Sykora, S., 2020. Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier. GEOPHYSICS 85, B249–B258. https://doi.org/10.1190/geo2019-0461.1
Li, X., Zhang, C., Behrens, H., Holtz, F., 2020. Lithos Calculating amphibole formula from electron microprobe analysis data using a machine learning method based on principal components regression. LITHOS 362–363, 105469. https://doi.org/10.1016/j.lithos.2020.105469
Martín-Fernández, J.A., Barceló-Vidal, C., Pawlowsky-Glahn, V., 2003. Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation. Math. Geol. 35, 253–278. https://doi.org/10.1023/A:1023866030544
McKay, G., Harris, J.R., 2016. Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada. Nat. Resour. Res. 25, 125–143. https://doi.org/10.1007/s11053-015-9274-z
Misra, S., Osogba, O., Powers, M., 2020. Unsupervised outlier detection techniques for well logs and geophysical data, Machine Learning for Subsurface Characterization. Elsevier Inc. https://doi.org/10.1016/b978-0-12-817736-5.00001-6
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., 2011. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830.
Prado, E.M.G., de Souza Filho, C.R., Carranza, E.J.M., Motta, J.G., 2020. Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data. Ore Geol. Rev. 124, 103611. https://doi.org/10.1016/j.oregeorev.2020.103611
Radford, D.D.G., Cracknell, M., Roach, M., Cumming, G., 2018. Geological Mapping in Western Tasmania Using Radar and Random Forests, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2018.2855207
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M., 2015. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol. Rev. 71, 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001
Rubo, R.A., de Carvalho Carneiro, C., Michelon, M.F., Gioria, R. dos S., 2019. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. J. Pet. Sci. Eng. 183. https://doi.org/10.1016/j.petrol.2019.106382
Sarbas, 2021. GEOROC - Geochemistry of Rocks if the Oceans and Cotinents [WWW Document]. URL http://georoc.mpch-mainz.gwdg.de/georoc/
Schramm, B., Jochum, K.P., Sarbas, B., Nohl, U., 2006. GEOROC and GeoReM—Linking the information of two Geological databases. Geochim. Cosmochim. Acta 70, A565. https://doi.org/10.1016/j.gca.2006.06.1045
Schroeder, M., Cornford, D., Farrimond, P., Cornford, C., 2008. Addressing missing data in geochemistry: A non-linear approach. Org. Geochem. 39, 1162–1169. https://doi.org/10.1016/j.orggeochem.2008.02.016
Schumacker, R., Tomek, S., 2013. Understanding Statistics Using R. Springer New York, New York, NY. https://doi.org/10.1007/978-1-4614-6227-9
Shannon, C.E., 1948. A Mathematical Theory of Communication. Bell Syst. Tech. J. 27, 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
Smiti, A., 2020. A critical overview of outlier detection methods. Comput. Sci. Rev. 38, 100306. https://doi.org/10.1016/j.cosrev.2020.100306
Vijayvargiya, A., Prakash, C., Kumar, R., Bansal, S., João, J.M., 2021. Human knee abnormality detection from imbalanced sEMG data. Biomed. Signal Process. Control 66. https://doi.org/10.1016/j.bspc.2021.102406
Zhang, Shuai, Carranza, E.J.M., Wei, H., Xiao, K., Yang, F., Xiang, J., Zhang, Shihong, Xu, Y., 2021. Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network. Nat. Resour. Res. 30, 1011–1031. https://doi.org/10.1007/s11053-020-09789-y