Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet

Hongfei Qian, Xiangchun Wang, Xuelei Chen and Zhu Yang
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Hongfei Qian: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Xiangchun Wang: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Xuelei Chen: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Zhu Yang: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China

Energies, 2022, vol. 15, issue 9, 1-15

Abstract: Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than by a conventional piezoelectric seismic exploration system. Considering that the conventional denoising method is time-consuming, this paper proposes a convolutional neural network (CNN) and a ResUNet network based on deep learning to suppress the noises. ResUNet is improved on the basis of CNN; it is composed of a feature extraction part, a feature reconstruction part and a residual block. Both CNN and ResUNet networks achieved obvious denoising effects on optical fiber towed streamer seismic data and improved the signal-to-noise ratio of data effectively. The ResUNet network has better denoising effects than CNN, even better than conventional denoising methods. The ResUNet network can solve the problem of gradient disappearance caused by network deepening; it recovered edge data well, and it has high efficiency compared with conventional denoising methods. Two evaluation indexes, relative error (RE) and similarity structure degree (SSIM), were introduced to compare the denoising effect of the ResUNet network with that of CNN. The experimental results showed that the performance of the ResUNet network in these two aspects is better than that of CNN.

Keywords: deep learning; ResUNet network; marine optical fiber towed streamer seismic; noise suppression; residual block; signal-to-noise ratio (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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