Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ...
Nowadays, the application of mathematical models in geology becomes more and more relevant. The steady trend towards the global digitalization has led to the possibility of using the most modern computational methods in the construction of mathematical models. Digitalization, further processing of d...
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Институт проблем управления им. В. А. Трапезникова РАН
2024
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Online Access: | https://dx.doi.org/10.25728/assa.2023.23.04.1507 http://search.rads-doi.org/project/14368/object/210075 |
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ftdatacite:10.25728/assa.2023.23.04.1507 2024-03-31T07:55:42+00:00 Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... Gabdrakhmanova, Nailia Klimtsev, Pavel 2024 https://dx.doi.org/10.25728/assa.2023.23.04.1507 http://search.rads-doi.org/project/14368/object/210075 en eng Институт проблем управления им. В. А. Трапезникова РАН http://search.rads-doi.org/project/14368 Text article-journal Journal Article ScholarlyArticle 2024 ftdatacite https://doi.org/10.25728/assa.2023.23.04.1507 2024-03-04T13:45:20Z Nowadays, the application of mathematical models in geology becomes more and more relevant. The steady trend towards the global digitalization has led to the possibility of using the most modern computational methods in the construction of mathematical models. Digitalization, further processing of digital data, their analysis and subsequent modeling contributes to the improvement of production efficiency. The purpose of this paper is the development of various methods of classification of kimberlite wells. The paper presents neural network, statistical and geometric mathematical models for solving the problem of kimberlite well classification. The problem was solved using geological and exploration data from wells drilled in the Süldükar and Ulakhan-Kurung-Yuryakh areas located in Western Yakutia. For the constructed models the estimations of the models' qualities were obtained, the comparative analysis of the models was carried out. The analysis of mathematical models showed that the most accurate models ... : Advances in Systems Science and Applications, Выпуск 4 2024, Pages 179-187 ... Text Yakutia DataCite Metadata Store (German National Library of Science and Technology) Kurung ENVELOPE(126.400,126.400,63.400,63.400) Kurung-Yuryakh ENVELOPE(147.382,147.382,68.980,68.980) Yuryakh ENVELOPE(145.658,145.658,59.865,59.865) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
English |
description |
Nowadays, the application of mathematical models in geology becomes more and more relevant. The steady trend towards the global digitalization has led to the possibility of using the most modern computational methods in the construction of mathematical models. Digitalization, further processing of digital data, their analysis and subsequent modeling contributes to the improvement of production efficiency. The purpose of this paper is the development of various methods of classification of kimberlite wells. The paper presents neural network, statistical and geometric mathematical models for solving the problem of kimberlite well classification. The problem was solved using geological and exploration data from wells drilled in the Süldükar and Ulakhan-Kurung-Yuryakh areas located in Western Yakutia. For the constructed models the estimations of the models' qualities were obtained, the comparative analysis of the models was carried out. The analysis of mathematical models showed that the most accurate models ... : Advances in Systems Science and Applications, Выпуск 4 2024, Pages 179-187 ... |
format |
Text |
author |
Gabdrakhmanova, Nailia Klimtsev, Pavel |
spellingShingle |
Gabdrakhmanova, Nailia Klimtsev, Pavel Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
author_facet |
Gabdrakhmanova, Nailia Klimtsev, Pavel |
author_sort |
Gabdrakhmanova, Nailia |
title |
Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
title_short |
Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
title_full |
Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
title_fullStr |
Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
title_full_unstemmed |
Machine Learning and Geometric Mathematical Models in Kimberlite Well Classification Problems ... |
title_sort |
machine learning and geometric mathematical models in kimberlite well classification problems ... |
publisher |
Институт проблем управления им. В. А. Трапезникова РАН |
publishDate |
2024 |
url |
https://dx.doi.org/10.25728/assa.2023.23.04.1507 http://search.rads-doi.org/project/14368/object/210075 |
long_lat |
ENVELOPE(126.400,126.400,63.400,63.400) ENVELOPE(147.382,147.382,68.980,68.980) ENVELOPE(145.658,145.658,59.865,59.865) |
geographic |
Kurung Kurung-Yuryakh Yuryakh |
geographic_facet |
Kurung Kurung-Yuryakh Yuryakh |
genre |
Yakutia |
genre_facet |
Yakutia |
op_relation |
http://search.rads-doi.org/project/14368 |
op_doi |
https://doi.org/10.25728/assa.2023.23.04.1507 |
_version_ |
1795037983795052544 |