Numerical dispersion mitigation neural network for seismic modeling

Autor:	Kirill Gadylshin, Dmitry Vishnevsky, Kseniia Gadylshina, Vadim Lisitsa
Rok vydání:	2022
Předmět:	Geophysics Geochemistry and Petrology
Zdroj:	GEOPHYSICS. 87:T237-T249
ISSN:	1942-2156 0016-8033
Popis:	We have developed a novel approach for seismic modeling combining conventional finite differences with deep neural networks. The method includes the following steps. First, a training data set composed of a small number of common-shot gathers is generated. The data set is computed using a finite-difference scheme with fine spatial and temporal discretization. Second, the entire set of common-shot seismograms is generated using an inaccurate numerical method, such as a finite-difference scheme on a coarse mesh. Third, the numerical dispersion mitigation neural network is trained and applied to the entire data set to suppress the numerical dispersion. We have tested the approach on two 2D models, illustrating a significant acceleration of seismic modeling.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d9406337437f4c5bac275ee4dadeda05 https://doi.org/10.1190/geo2021-0242.1 Zobrazit plný text záznamu