Optimization of the Training Dataset for Numerical Dispersion Mitigation Neural Network Full article
| Journal |
Lecture Notes in Computer Science
ISSN: 0302-9743 , E-ISSN: 1611-3349 |
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| Output data | Year: 2022, Volume: 13378 LNCS, Pages: 295-309 Pages count : 15 DOI: 10.1007/978-3-031-10562-3_22 | ||||
| Tags | Deep learning; Numerical dispersion; Seismic modelling | ||||
| Authors |
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| Affiliations |
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Funding (4)
| 1 | Russian Science Foundation | 22-11-00004 |
| 2 | Russian Science Foundation | 22-21-00738 |
| 3 | Russian Science Foundation | 19-77-20004 |
| 4 | Президент РФ | MK-3947.2021.1.5 |
Abstract:
We present an approach to construct the training dataset for the numerical dispersion mitigation network (NDM-net). The network is designed to suppress numerical error in the simulated seismic wavefield. The training dataset is the wavefield simulated using a fine grid, thus almost free from the numerical dispersion. Generation of the training dataset is the most computationally intense part of the algorithm, thus it is important to reduce the number of seismograms used in the training dataset to improve the efficiency of the NDM-net. In this work, we introduce the discrepancy between seismograms and construct the dataset, so that the discrepancy between the dataset and any seismogram is below the prescribed level.
Cite:
Gadylshin K.
, Lisitsa V.
, Gadylshina K.
, Vishnevsky D.
Optimization of the Training Dataset for Numerical Dispersion Mitigation Neural Network
Lecture Notes in Computer Science. 2022. V.13378 LNCS. P.295-309. DOI: 10.1007/978-3-031-10562-3_22 Scopus OpenAlex
Optimization of the Training Dataset for Numerical Dispersion Mitigation Neural Network
Lecture Notes in Computer Science. 2022. V.13378 LNCS. P.295-309. DOI: 10.1007/978-3-031-10562-3_22 Scopus OpenAlex
Dates:
| Published online: | Aug 4, 2022 |
Identifiers:
| Scopus: | 2-s2.0-85135909737 |
| OpenAlex: | W4289601211 |