南京大学学报（自然科学版）2024，Vol.60Issue(5)：763-775,13.DOI:10.13232/j.cnki.jnju.2024.05.007
融合四种注意力机制的多尺度残差地震数据去噪网络
Multiscale residual seismic data denoising network with fusion of quadruple attentions
摘要
Abstract
Random noise denoising is an important step in seismic data processing.Many methods based on convolutional neural networks only consider single-scale features and cannot adaptively linearly aggregate seismic data features,resulting in difficulties in removing complex noise and protecting weak signals.In this paper,a multi-scale convolutional residual seismic denoising network fusing quadruple attention mechanisms(MARN)is proposed.It consists of three main parts:a single-scale feature extraction layer,a multi-scale feature fetching layer,and a feature recovery layer.The single-scale feature extraction layer uses a single identical convolutional kernel to extract global coarse features.The multiscale feature extraction layer contains multiple residual multi-scale attention feature extraction blocks(RMSAB),each consisting of multiple multiaxial attention multi-scale feature fusion blocks(MAFB).The MAFBs contain three structures:the feature extraction structure extracts the local fine features through the four attentional mechanisms,the feature fusion structure fuses the features extracted by the four attentional mechanisms,and the feature transfer structure delivers the features to the feature recovery layer.The feature recovery layer fuses the extracted single-scale and multi-scale features to obtain denoised seismic data.The experimental results show that MARN can not only remove random noise in a more targeted way,but also retain the weak signals better.关键词
去除随机噪声/卷积神经网络/多注意力机制/多尺度特征/残差网络Key words
random noise denoising/convolutional neural network/multi-attention mechanisms/multi-scale features/residual network分类
天文与地球科学引用本文复制引用
高磊,樊星灿,乔昊炜,闵帆,杨梅..融合四种注意力机制的多尺度残差地震数据去噪网络[J].南京大学学报（自然科学版）,2024,60(5):763-775,13.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) （23XNSYSX0084）
