Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Applied Computer Systems 2017
Yan Kuchin, Jānis Grundspeņķis

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.

Atslēgas vārdi
Data mining, machine learning, well logging surveys
DOI
10.1515/acss-2017-0014

Kuchin, Y., Grundspeņķis, J. Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan. Applied Computer Systems, 2017, 22, 21.-27.lpp. ISSN 2255-8683. e-ISSN 2255-8691. Pieejams: doi:10.1515/acss-2017-0014

Publikācijas valoda
English (en)