应用注意力增强特征UNet的地震速度建模OA

Accurate underground velocity information is crucial for seismic imaging in complex area.While ex-isting seismic waveform inversion techniques are highly accurate,they have shortcomings such as high computa-tional amount and reliance on initial models.Currently,deep learning technology experiences rapid advance-ments in various fields and has successfully been applied to nonlinear seismic inversion.However,conven-tional end-to-end deep learning networks struggle to establish a multi-scale physical coupling relationship be-tween velocity parameters and seismic records.To this end,this paper proposes a hybrid network AER-UNet,which reorganizes the encode and decoder structures and adds an attention mechanism-based jumping connection module on this basis.This approach effectively obtains key spatial information from seismic records and en-hances the representation of the subtle structures in velocity fields,thus accurately capturing the characteristics of underground medium velocity parameters.An appropriate number of random velocity models should be built in the network training phase to simulate the true structure of the underground medium and thus obtain the accu-rate mapping relationship between velocity models and seismic records.Additionally,developing new loss func-tions can help improve the computational accuracy of velocity modeling.By carrying out numerical experiments using the SEG/EAGE thrust model,the effectiveness of the hybrid network for velocity modeling is evaluated.Compared to FWI and other deep learning networks,this method can more efficiently and accurately rebuild un-derground velocity models.