融合四种注意力机制的多尺度残差地震数据去噪网络OA北大核心CSTPCD

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.