Efficient Seismic Data Denoising via Deep Learning With Improved MCA-SCUNet

In hydrocarbon exploration, seismic data collected in the field inevitably encounters noise interference, which subsequently affects the data processing and interpretation. Recently, deep learning methods have gained widespread popularity in seismic denoising. Among these methods, the U-Net has shown some potential, but its performance in complex noise suppression needs further improvement due to the limitations of the U-Net structure. Moreover, the majority of existing noise suppression methods primarily focus on synthetic noises with single characteristics, such as Gaussian random noise and linear interference. To devise methods that can effectively suppress more intricate field noise, this paper proposes a novel noise suppression method based on an encoder-decoder architecture called Multiscale Channel Attention Swin Conv UNet. Notably, it enhances the U-Net through the integration of the following two modules: (1) Swin-Conv Block, which replaces the convolution operation of U-Net and integrates the non-local modeling ability of Swin Transformer and the local modeling ability of residual connection convolution layers to achieve multi-dimensional feature extraction; (2) Multiscale Channel Attention Block, which replaces skip connection modules between the encoder and decoder in the U-Net with multi-channel feature fusion to capture more complex channel dependencies. This paper evaluates the proposed algorithm on both synthetic and field seismic data, and compares the results with several established denoising methods. Our algorithm enhances the network's noise perception capabilities and improves signal-to-noise ratio and structural similarity index measure of seismic data. Finally, a concise discussion on the limitations of our method and potential avenues for enhancement is provided.