<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.</p></abstract>

The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Branch Office “Geotechnocentr” of JSC Volkovgeologija

Applied Computer Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of...

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Online veröffentlicht: 27. Dez. 2017

Seitenbereich: 21 - 27

DOI: https://doi.org/10.1515/acss-2017-0014

Schlüsselwörter
Data mining, machine learning, well logging surveys

© 2017 Yan Kuchin et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Online veröffentlicht: 27. Dez. 2017

Seitenbereich: 21 - 27

DOI: https://doi.org/10.1515/acss-2017-0014

SchlüsselwörterData mining, machine learning, well logging surveys

© 2017 Yan Kuchin et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Data mining, machine learning, well logging surveys