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  • 标题:Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan
  • 本地全文:下载
  • 作者:Yan Kuchin ; Jānis Grundspeņķis
  • 期刊名称:Applied Computer Systems
  • 印刷版ISSN:2255-8691
  • 出版年度:2017
  • 卷号:22
  • 期号:1
  • 页码:21-27
  • DOI:10.1515/acss-2017-0014
  • 语种:English
  • 出版社:Walter de Gruyter GmbH
  • 摘要: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.
  • 关键词:Data mining ; machine learning ; well logging surveys
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