Seismic data acquired from mature industrial areas frequently contain a large amount of local strong noise with high amplitude due to the continuous operation of production equipment.However,such local strong noise can be hardly suppressed using conventional denoising methods.This study integrated dilated convolution(DC) and U-Net into a DC-UNet network for suppressing local strong noise.For the circular DC blocks at the front end of the DC-Unet network,a circularly expanded DC kernel was used to extract the features of strong noise at different scales,with the receptive field being expanded.Meanwhile,an encoder was used at the back end of the network to extract the features of strong noise and restore the details of strong noise.Subsequently,the DC-UNet network was employed to perform a nonlinear mapping from noisy data to noise.On this basis,strong noise was suppressed by subtracting the learned strong noise from the noisy data.As indicated by the experimental results of synthetic and real data obtained from the training using the PyTorch framework in the GPU environment,the DC-UNet network can effectively suppress the local strong noise and improve the signal-to-noise ratio compared with DnCNN,U-Net,and PCA-UNet networks.
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