DAS Noise Suppression Network Based on Distributing-Local-Attention Expansion

Distributed acoustic sensing (DAS) has been progressively used in acquiring vertical seismic profiles. However, DAS signals are susceptible to be contaminated by diverse noise, causing many difficulties in the interpretation of DAS VSP. Most of the existing methods for DAS noise suppression rely on either global or local information for the extraction of signal features. They neglected that both local detail and long-distance relevant features are required for denoising. To address this issue, we propose a U-shaped network with a combination of convolutional neural networks (CNNs) and refining transformers, named Urefiner. We employ CNN as a preprocessing step for the transformer model. The transformer model relies on the attention map to extract global features, while the CNN module will aggregate similar features within the attention map to facilitate local information processing. To facilitate the fusion of local information and global information, the distributing-local-attention (DLA) module is induced to calculate the weighted aggregation in the attention map between CNN and transformer, which can improve the effective receptive field of the network in local areas. Additionally, to improve the network’s attention to seismic signals for signal protection, we introduce a learnable linear matrix to expand attention map. It can aggregate the information acquired from different attention heads into a learnable weight matrix for the attention calculation. This linear weighting scheme can promote the interconnections among diverse attention heads, thereby facilitating the fusion of extracted information. Based on the above designs, the proposed Urefiner can augment the effective receptive field to amplify the significance of signal features in the attention map. Experimental results show that the network can effectively suppress various noises in DAS VSP and accurately protecting weak seismic signals.