Numerical Dispersion Mitigation Neural Network with the Model-Based Training Dataset Optimization Full article
| Conference |
The International Conference on Computational Sciences and its Applications 03-06 Jul 2023 , Athens |
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| Source | Computational Science and Its Applications – ICCSA 2023 Workshops
Athens, Greece, July 3–6, 2023, Proceedings Compilation, Springer. 2023. ISBN 9783031371103. |
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| Journal |
Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349 |
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| Output data | Year: 2023, Volume: 14106, Pages: 19-30 Pages count : 12 DOI: 10.1007/978-3-031-37111-0_2 | ||||
| Tags | Deep learning · seismic modelling · numerical dispersion | ||||
| Authors |
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| Affiliations |
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Funding (2)
| 1 | Russian Science Foundation | 22-11-00104 |
| 2 | Russian Science Foundation | 22-11-00004 |
Abstract:
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.
Cite:
Gondyul E.
, Lisitsa V.
, Gadylshin K.
, Vishnevsky D.
Numerical Dispersion Mitigation Neural Network with the Model-Based Training Dataset Optimization
In compilation 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
In compilation 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
Dates:
| Published print: | Jun 29, 2023 |
| Published online: | Jun 29, 2023 |
Identifiers:
| Scopus: | 2-s2.0-85164981562 |
| OpenAlex: | W4382366601 |