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)