Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

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 re...

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Main Authors: Kuchin Yan, Grundspeņķis Jānis
Format: Article
Language:English
Published: Sciendo 2017-12-01
Series:Applied Computer Systems
Subjects:
data mining
machine learning
well logging surveys
Online Access:https://doi.org/10.1515/acss-2017-0014
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spelling doaj-27e53fe492174dd38b2cc42991120f532021-09-06T19:39:41ZengSciendoApplied Computer Systems2255-86912017-12-01221212710.1515/acss-2017-0014acss-2017-0014Machine Learning Methods for Identifying Composition of Uranium Deposits in KazakhstanKuchin Yan0Grundspeņķis Jānis1Branch Office “Geotechnocentr” of JSC Volkovgeologija, KazakhstanRiga Technical University, LatviaThe 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.https://doi.org/10.1515/acss-2017-0014data miningmachine learningwell logging surveys
collection DOAJ
language English
format Article
sources DOAJ
author Kuchin Yan
Grundspeņķis Jānis
spellingShingle Kuchin Yan
Grundspeņķis Jānis
Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
Applied Computer Systems
data mining
machine learning
well logging surveys
author_facet Kuchin Yan
Grundspeņķis Jānis
author_sort Kuchin Yan
title Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
title_short Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
title_full Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
title_fullStr Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
title_full_unstemmed Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
title_sort machine learning methods for identifying composition of uranium deposits in kazakhstan
publisher Sciendo
series Applied Computer Systems
issn 2255-8691
publishDate 2017-12-01
description 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.
topic data mining
machine learning
well logging surveys
url https://doi.org/10.1515/acss-2017-0014
work_keys_str_mv AT kuchinyan machinelearningmethodsforidentifyingcompositionofuraniumdepositsinkazakhstan
AT grundspenkisjanis machinelearningmethodsforidentifyingcompositionofuraniumdepositsinkazakhstan
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