Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Yan Kuchin; Jānis Grundspeņķis

doi:10.1515/acss-2017-0014

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Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Applied Computer Systems

Volume 22 (2017): Issue 1 (December 2017)

By: Yan Kuchin and Jānis Grundspeņķis

Open Access

|Dec 2017

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.

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Articles in this issue

DOI: https://doi.org/10.1515/acss-2017-0014 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683

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Language: English

Page range: 21 - 27

Published on: Dec 27, 2017

Published by: Riga Technical University

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

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Artificial intelligence,

Information technology,

Project management,

Software development

© 2017 Yan Kuchin, Jānis Grundspeņķis, published by Riga Technical University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 22 (2017): Issue 1 (December 2017)