A comparative analysis between the Geostatistics and Machine Learning methods for mineral resource estimation

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DOI:

https://doi.org/10.70597/ijget.v2i1.380

Palavras-chave:

Ordinary Kriging, Geostatistics, Random Forests, Machine Learning

Resumo

It is presented in this article two methods for estimating minerals resources. The first one is well known in the literature of mining engineering, it is called Ordinary Kriging, which is one of most used algorithms in Geostatistics to deal with regionalized variables. The second one is not yet so popular to be used for estimating minerals resources, although its applicability to be very effective for this task. This method is called Random Forests, which is an algorithm of Machine Learning. Thus, its analysis will focus on the construction procedure of block models of a copper mineral deposit using both methodologies.

Referências

Curi, A., 2014. Minas a céu aberto: planejamento de lavra. Oficina de Textos, pp.87-104.

Goovaerts, P., 1997. Geostatistics for natural resources evaluation. Oxford University Press on Demand, pp.131-137.

Hastie, T., Tibshirani, R., and Friedman, J., 2009. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, pp.587-603. https://doi.org/10.1007/978-0-387-84858-7

Houlding, S., 2000. Practical geostatistics: modeling and spatial analysis. Manual. Springer Science & Business Media.

Isaaks, E.H., and Srivastava, M.R., 1989. Applied geostatistics (No. 551.72 ISA), pp.370-397.

Journel, A.G., and Huijbregts, C.J., 1978. Mining geostatistics. London: Academic press.

Samuel, A.L., 1959. Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), pp.210-229. https://doi.org/10.1147/rd.33.0210

Yamamoto, J.K., and Landim, P.M.B., 2015. Geoestatística: conceitos e aplicações. Oficina de textos.

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Publicado

2020-10-31

Como Citar

Silva, R. A. da (2020) “A comparative analysis between the Geostatistics and Machine Learning methods for mineral resource estimation”, International Journal of Geoscience, Engineering and Technology, 2(1), p. 14–22. doi: 10.70597/ijget.v2i1.380.

Edição

v. 2 n. 1 (2020): International Journal of Geoscience, Engineering and Technology

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Copyright (c) 2020 International Journal of Geoscience, Engineering and Technology

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