Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention

Suppression of random noise can effectively improve the signal-to-noise ratio (SNR) of seismic data. In recent years, convolutional neural network (CNN)-based deep learning methods have shown significant performance in seismic data denoising. However, the convolution operation in CNN usually can only capture local information due to the limitation of receptive field while cannot establish long-distance connections of global information, which may lead to the loss of detailed information. For the problem of denoising seismic data, an efficient Transformer model with gradient prediction and parameter-free attention (ETGP) is proposed. Firstly, a multi-Dconv head “transposed” attention is introduced in place of the traditional multi-head attention, which can compute the attention between channels to represent the global information, and alleviate the problem of high complexity of the traditional multi-head attention. Secondly, a parameter-free attention feed-forward network is proposed, which can compute the attention weight considering both the spatial and the channel dimensions without adding parameters to the network. Lastly, a gradient prediction network (GPN) is designed to extract edge information and adaptively add the information to the input of the parallel Transformer to obtain high-quality seismic data. Experiments are conducted on synthetic and field data, and the proposed method in this paper is compared with classical and advanced denoising methods. The results show that the ETGP denoising method not only suppresses random noise more effectively, but also has significant advantages in terms of weak signal retention and event continuity.