PT Journal
AU Merembayev, T
   Yunussov, R
   Yedilkhan, A
TI Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits
SO Procedia Computer Science
PY 2019
BP 46
EP 52
VL 150
DE classification; geophysics logging data; machine learning; stratigraphy; uranium deposit
AB 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.
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