MSAACNN for intense noise suppression in DAS-VSP records

Abstract

Abstract Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Keywords

• Geophysical exploration
• Distributed optical fiber acoustic sensor
• Complex background noise
• Convolutional neural network