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Numerical Dispersion Mitigation Neural Network with the Model-Based Training Dataset Optimization Научная публикация

Конференция The International Conference on Computational Sciences and its Applications
03-06 июл. 2023 , Athens
Сборник Computational Science and Its Applications – ICCSA 2023 Workshops Athens, Greece, July 3–6, 2023, Proceedings
Сборник, Springer. 2023. ISBN 9783031371103.
Журнал Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349
Вых. Данные Год: 2023, Том: 14106, Страницы: 19-30 Страниц : 12 DOI: 10.1007/978-3-031-37111-0_2
Ключевые слова Deep learning · seismic modelling · numerical dispersion
Авторы Gondyul Elena 1 , Lisitsa Vadim 1 , Gadylshin Kirill 2 , Vishnevsky Dmitry 1
Организации
1 Institute of Petroleum Geology and Geophysics SB RAS, Novosibirsk, Russia
2 Sobolev Institute of Mathematics SB RAS, Novosibirsk, Russia

Информация о финансировании (2)

1 Российский научный фонд 22-11-00104
2 Российский научный фонд 22-11-00004

Реферат: A neural network is used to approximate the transition operator from seismic data modeled on a large computational grid to data obtained on a small one. Thus, we obtain an effective way of suppressing numerical dispersion in numerically modeled seismic fields. This article discusses a method for constructing an optimal training dataset based on the properties of a velocity model. We build a distance matrix for the parts of the model that correspond to the positions of the sources and build a dataset in such a way that the distance between the training set and all sources is limited.
Библиографическая ссылка: Gondyul E. , Lisitsa V. , Gadylshin K. , Vishnevsky D.
Numerical Dispersion Mitigation Neural Network with the Model-Based Training Dataset Optimization
В сборнике Computational Science and Its Applications – ICCSA 2023 Workshops Athens, Greece, July 3–6, 2023, Proceedings. – Springer., 2023. – Т.Part III. – C.19-30. – ISBN 9783031371103. DOI: 10.1007/978-3-031-37111-0_2 Scopus OpenAlex
Даты:
Опубликована в печати: 29 июн. 2023 г.
Опубликована online: 29 июн. 2023 г.
Идентификаторы БД:
Scopus: 2-s2.0-85164981562
OpenAlex: W4382366601
Цитирование в БД:
БД Цитирований
OpenAlex 3
Scopus 4
Альметрики: