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 |
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Сборник | Computational Science and Its Applications – ICCSA 2023 Workshops
Athens, Greece, July 3–6, 2023, Proceedings Сборник, Springer. 2023. ISBN 9783031371103. |
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Журнал |
Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349 |
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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 |
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Организации |
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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
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 |