计算机科学与探索2025，Vol.19Issue(5)：1342-1352,11.DOI:10.3778/j.issn.1673-9418.2405052
融合梯度预测和无参注意力的高效地震去噪Transformer
Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention
摘要
Abstract
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 limita-tion 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 predic-tion 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 in-formation,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.关键词
地震数据去噪/卷积神经网络/Transformer/注意力模块/梯度融合Key words
seismic data denoising/convolutional neural network/Transformer/attention module/gradient fusion分类
信息技术与安全科学引用本文复制引用
高磊,乔昊炜,梁东升,闵帆,杨梅..融合梯度预测和无参注意力的高效地震去噪Transformer[J].计算机科学与探索,2025,19(5):1342-1352,11.基金项目
南充市-西南石油大学市校科技战略合作专项资金(23XNSYSX0084). This work was supported by the Special Fund for Science and Technology Strategic Cooperation Between Nanchong City and South-west Petroleum University(23XNSYSX0084). （23XNSYSX0084）
