Research on the construction method of speed model based on Attention-UNet network
As oil and gas resources continue to be explored and developed,relatively easy-to-reach oil and gas plays are gradually being built up,and the focus of seismic exploration research has shifted to more profound and more structurally com-plex areas in the subsurface.Traditional seismic velocity modeling methods face stability,accuracy,and computational efficiency challenges.Therefore,this paper proposes a deep learning velocity modeling method based on attention to UNet networks to map seismic data to velocity models.The method obtains reflection wave data through finite-difference forward modeling.It uses the reflection wave data and the corresponding velocity model(label)pair as the input to the attention UNet network to estab-lish the mapping relationship between the seismic data and the velocity model.After network training,the velocity model is esti-mated for the newly input seismic data.Numerical results show that the method also exhibits good performance compared to conventional FWI;once the training of the attention-based UNet network model is completed,velocity modeling of subsurface structures similar to the velocity structure in the training set can be performed quickly without extensive computation,providing higher computational efficiency than conventional methods.The method has good extension value in building many velocity mod-els.
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