TY  - JOUR
AU  - Merembayev, T.
AU  - Yunussov, R.
AU  - Yedilkhan, A.
PY  - 2019//
TI  - Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits
JO  - Procedia Computer Science
SP  - 46
EP  - 52
VL  - 150
KW  - classification
KW  - geophysics logging data
KW  - machine learning
KW  - stratigraphy
KW  - uranium deposit
N2  - 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.
SN  - 1877-0509
UR  - https://www.sciencedirect.com/science/article/pii/S1877050919303539
N1  - exported from refbase (http://www.uhydro.de/base/show.php?record=113), last updated on Fri, 26 Jan 2024 13:19:04 +0100
ID  - Merembayev_etal2019
ER  -