Talk · Procedia Computer Science · January 1, 2019

Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits

Timur Merembayev, Rassul Yunussov, Amirgaliyev Yedilkhan

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.

Geochemistry and Geologic MappingGeological Modeling and AnalysisData Mining and Machine Learning Applicationsuranium depositsrandom forestgradient boosting

Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits

Abstract

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