Full waveform inversion based on dynamic data matching of convolutional wavefields

Cycle skipping problem caused by the absent of low frequencies and inaccurate initial model makes full waveform inversion (FWI) deviate from the true model. A novel method is proposed to mitigate cycle skipping phenomenon by dynamic data matching which improves the matching of synthetic and observed...

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Autors principals:	Liming Zhou, Shiqi Dong, Liguo Han, Pan Zhang, Yong Hu
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2023-04-01
Col·lecció:	Frontiers in Earth Science
Matèries:	full waveform inversion (FWI) Gaussian convolutional kernels features extraction dynamic data matching optimal matching travel-time differences constraint
Accés en línia:	https://www.frontiersin.org/articles/10.3389/feart.2023.1134871/full
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