Noise suppression using deep learning methods is mostly based on convolutional neural networks.The convolution operation u-sing the convolution kernel extracts local features,instead of global features,of seismic data;thus,random noises could not be eliminated perfectly.In addition,L1 and L2 loss functions tend to generate an over-smoothed network model and consequent false events and errone-ously high values of peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).To address this issue,we develop a denoising method based on the Swin-Transformer and generative adversarial network(ST-GAN).The Swin-Transformer functions as the generative network in the GAN for denoising,and the discrimination network is based on a convolutional neural network.Global features of seismic da-ta,which could be obtained owing to the self-attention mechanism of the Transformer,and local features derived from the convolutional neural network may complement each other for the better performance of the network model.The use of adversarial loss makes it possible to recover more details by applying the network model and mitigate artificial events caused by over-smoothing.The comparative analysis shows that our approach is superior to other denoising methods in feature extraction and signal-to-noise ratio because random noises are ef-fectively reduced and meanwhile more details of seismic data are recovered and preserved.
deep learningnoise suppressionSwin-Transformerself-attentionGANCNNloss function
国家科技期刊平台
NSTL
维普
万方数据
