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Machine learning algorithms for stratigraphy classification on uranium deposits

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Abstract
Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost.
Keywords
stratigraphy, classification, machine learning, uranium deposit, geophysics logging data

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MLA
Merembayev, Timur, et al. “Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits.” PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18), edited by A. Diveev et al., vol. 150, 2019, pp. 46–52, doi:10.1016/j.procs.2019.02.010.
APA
Merembayev, T., Yunussov, R., & Yedilkhan, A. (2019). Machine learning algorithms for stratigraphy classification on uranium deposits. In A. Diveev, I. Zelinka, F. L. Pereira, & E. Nikulchev (Eds.), PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18) (Vol. 150, pp. 46–52). St Petersburg, Russia. https://doi.org/10.1016/j.procs.2019.02.010
Chicago author-date
Merembayev, Timur, Rassul Yunussov, and Amirgaliyev Yedilkhan. 2019. “Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits.” In PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18), edited by A. Diveev, I. Zelinka, F.L. Pereira, and E. Nikulchev, 150:46–52. https://doi.org/10.1016/j.procs.2019.02.010.
Chicago author-date (all authors)
Merembayev, Timur, Rassul Yunussov, and Amirgaliyev Yedilkhan. 2019. “Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits.” In PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18), ed by. A. Diveev, I. Zelinka, F.L. Pereira, and E. Nikulchev, 150:46–52. doi:10.1016/j.procs.2019.02.010.
Vancouver
1.
Merembayev T, Yunussov R, Yedilkhan A. Machine learning algorithms for stratigraphy classification on uranium deposits. In: Diveev A, Zelinka I, Pereira FL, Nikulchev E, editors. PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18). 2019. p. 46–52.
IEEE
[1]
T. Merembayev, R. Yunussov, and A. Yedilkhan, “Machine learning algorithms for stratigraphy classification on uranium deposits,” in PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS’18), St Petersburg, Russia, 2019, vol. 150, pp. 46–52.
@inproceedings{8713828,
  abstract     = {{Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words - it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost.}},
  author       = {{Merembayev, Timur and Yunussov, Rassul and Yedilkhan, Amirgaliyev}},
  booktitle    = {{PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2018 (INTELS'18)}},
  editor       = {{Diveev, A. and Zelinka, I. and Pereira, F.L. and Nikulchev, E.}},
  issn         = {{1877-0509}},
  keywords     = {{stratigraphy,classification,machine learning,uranium deposit,geophysics logging data}},
  language     = {{eng}},
  location     = {{St Petersburg, Russia}},
  pages        = {{46--52}},
  title        = {{Machine learning algorithms for stratigraphy classification on uranium deposits}},
  url          = {{http://dx.doi.org/10.1016/j.procs.2019.02.010}},
  volume       = {{150}},
  year         = {{2019}},
}

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