Яндекс.Метрика

Elena Gondyul, Kirill Gadylshin, Vadim Lisitsa, Dmitry Vishnevsky

Том: Computational Science and Its Applications - ICCSA 2024 Workshops. 24rd International Conference (Hanoi, Vietnam, July 1-4, 2024). Proceedings, Part III
Том: 14817 , Год издания: 2024
Многотомное издание: Computational Science and Its Applications - ICCSA 2024 Workshops. 24rd International Conference (Hanoi, Vietnam, July 1-4, 2024). Proceedings, Part III
Страницы: 352-366

Аннотация

The paper discusses the extension of NDM-net (Numerical Dispersion Mitigation neural network) to pseudo-3D cases and the construction of training datasets. NDM-net is initially designed to reduce numerical dispersion in seismic data, which are generated as a result of simulating elastic waves. Previously, seismograms on a coarse grid with numerical dispersion and a certain number of seismograms on a fine grid are calculated to form a training sample. The paper discusses three approaches for building a representative sample to speed up the learning process in case of pseudo-3D. Additionally, the paper discusses the combination of metrics based on statistical analysis.
индекс в базе ИАЦ: 011256