Seismic velocity inversion method based on feature enhancement U-Net
The challenge faced by seismic velocity inversion methods based on deep neural networks is that the weak semantic mapping correspondence between seismic data in the time domain and model information in the spatial domain leads to a high degree of multiplicity.Additionally,neural networks lack effective guidance in mapping seismic data to velocity models,making them susceptible to noise interference and thus affecting inver-sion accuracy.Therefore,a seismic velocity inversion method based on feature enhancement U-Net is proposed.Firstly,by integrating the features of multi-shot seismic data,the spatial relationship between the seismic time series signal input to the network and the corresponding velocity model becomes more apparent.Subsequently,based on the concept of multi-scale feature fusion,modules with convolutional kernels of varying sizes are de-signed to bolster the network's capacity for learning effective features.Next,attention gates are used to guide the network and enhance the features that the network focuses on.Finally,based on the bottleneck residual and pre-activation,a pre-activation bottleneck residual is incorporated into the network,to avoid gradient disap-pearance and network degradation.The experiment shows that this method has higher accuracy in seismic ve-locity inversion and performs well in noise testing.It has a certain generalization ability.
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