A method for reconstructing consecutively missing seismic data based on recurrent feature reasoning

Objective Due to the constraints of natural environments like rapids, rifts, and high mountains, the acquired seismic data are often challenged by consecutive missing, affecting subsequent seismic data processing and geologic analysis. Hence, it is necessary to reconstruct the missing data through interpolation.

Methods This study proposed a method for reconstructing consecutively missing seismic data based on recurrent feature reasoning. First, the missing seismic data undergo partial convolution operations, in which the weight of the convolution results is adaptively adjusted based on the proportion of valid feature map data in the receptive field, avoiding invalid convolution operations on consecutively missing seismic channels. Second, the missing parts are progressively reconstructed through recurrent feature reasoning. Partial convolution operations and recurrent feature reasoning are alternated until all missing data are reconstructed. Finally, the reconstructed features generated in each iteration are integrated through feature fusion, ensuring accurate reasoning. To enhance the model's ability to learn the texture details of consecutively missing areas, the texture loss and mean square error (MSE) functions are combined as a hybrid loss function to further increase the reconstruction accuracy.

Results and Conclusions  Key findings are as follows: (1) The proposed method based on recurrent feature reasoning can effectively reconstruct the consecutively missing seismic data, with the signal-to-noise ratio (SNR) increased to 28.15 dB on top of the original 14.89 dB for the missing data. (2) In multiple reconstruction experiments focusing on 30 to 80 consecutively missing seismic channels, the reconstruction results demonstrate that the proposed method outperforms the U-Net method in terms of assessment indices like SNR, structural similarity, and MSE. The effectiveness of the proposed method is further verified by the reconstruction effects of the proposed method tested on six different public datasets. (3) As revealed by the impacts of the size of the partial convolution kernel on the reconstruction results investigated through comparative experiments, the reconstruction results manifest a higher SNR and a shorter iteration time when the partial convolution kernel measures 3×3. The results of this study provide a novel approach for the reconstruction of consecutively missing seismic data.