Hausdorff-distance-based training dataset construction for numerical dispersion mitigation neural network Научная публикация
| Журнал |
Computers and Geosciences
ISSN: 0098-3004 |
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| Вых. Данные | Год: 2023, Том: 180, Номер статьи : 105438, Страниц : 9 DOI: 10.1016/j.cageo.2023.105438 | ||||
| Ключевые слова | Seismic modeling, Numerical dispersion, Deep learning, Hausdorff distance | ||||
| Авторы |
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| Организации |
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Информация о финансировании (2)
| 1 | Российский научный фонд | 22-11-00004 |
| 2 |
Министерство науки и высшего образования РФ Математический центр в Академгородке |
075-15-2019-1613, 075-15-2022-281 |
Реферат:
We present a novel approach for constructing the training dataset for the Numerical Dispersion Mitigation neural network (NDM-net). The NDM-net is a multi-step approach to reduce numerical error in seismic modeling. First, a coarse grid is used to simulate the entire dataset with lower accuracy quickly. A fine grid is then used to simulate selected cases with higher accuracy. Next, the high-accuracy solutions are used together with corresponding low-accuracy solutions as a training dataset for the neural network. Finally, the neural network is used to improve the accuracy of the entire dataset of low-accuracy solutions. Running highaccuracy simulations for the training dataset is the most time-consuming step of the NDM-net approach. Thus, reducing the training dataset may significantly improve the NDM-net performance. In this study, we propose a training dataset construction method that maintains the Hausdorff distance between the training dataset and the complete dataset, allowing the training dataset to be generated from as few as 3%–5% of the total number of sources.
Библиографическая ссылка:
Gadylshin K.
, Lisitsa V.
, Vishnevsky D.
, Gadylshina K.
Hausdorff-distance-based training dataset construction for numerical dispersion mitigation neural network
Computers and Geosciences. 2023. V.180. 105438 :1-9. DOI: 10.1016/j.cageo.2023.105438 WOS Scopus РИНЦ OpenAlex
Hausdorff-distance-based training dataset construction for numerical dispersion mitigation neural network
Computers and Geosciences. 2023. V.180. 105438 :1-9. DOI: 10.1016/j.cageo.2023.105438 WOS Scopus РИНЦ OpenAlex
Даты:
| Поступила в редакцию: | 18 авг. 2022 г. |
| Принята к публикации: | 19 авг. 2023 г. |
| Опубликована в печати: | 24 авг. 2023 г. |
| Опубликована online: | 24 авг. 2023 г. |
Идентификаторы БД:
| Web of science: | WOS:001070880200001 |
| Scopus: | 2-s2.0-85171525355 |
| РИНЦ: | 63244826 |
| OpenAlex: | W4386122715 |