In the field of acoustic sensing, compared with traditional acoustic sensors, fiber-optic distributed acoustic sensing (DAS) system is a revolutionary technology with many unique capabilities, such as high sensitivity, large sensing scale, real-time dynamic strain detection, excellent compatibility to standard optical fibers. However, DAS is limited by the single-component sensing characteristic of fiber itself, 3-component (3C) DAS technology is still a major challenge to date. 3C-DAS is of great significance to acoustic target tracking aloft and underwater, as well as seismic exploration underground. In this paper, we demonstrate a 3C fiber-optic quasi-distributed acoustic sensing (QDAS) system to detect 3C strains applied to optical fibers, which are mounted on 3C elastomers. 3C strain changes when placing the acoustic source at different positions. We use an ultra-sensitive DAS (uDAS) system with ~5 pε/√Hz strain sensitivity to detect the acoustic field. When the acoustic source is placed 0.5m away from the elastomers, 3C strain detected by uDAS are 5.70 nε, 6.37 nε and 35.88 nε, respectively. The experimental results verify the feasibility of the proposed 3C-QDAS scheme.
KEYWORDS: Interference (communication), Signal to noise ratio, Denoising, Feature extraction, Signal attenuation, Gallium nitride, Signal processing, Computer programming, Signal generators, Databases
The fiber-optic distributed acoustic sensing (DAS) technology provides a highly efficient method for geophysical exploration, in which a fiber cable is equivalent to thousands of receivers with high density acquisition in space. However, the data interpretation for the vertical seismic profile (VSP) obtained from DAS is deteriorated by noises. Therefore, in this paper a noise reduction method with attention-aided generative adversarial network (GAN) is proposed. It integrates three sub networks: feature extractor, generator and discriminator. Specifically, in the feature extractor, the multi-head self-attention mechanism is used to generate a spatial attention weight matrix to extract the key information of the noises quickly. Then the original DAS-VSP signal and the spatial attention weight matrix are fed into the generator, and the noise reduction of original DAS-VSP signal is realized by the adversarial mechanism between the generator and the discriminator. A total of 80 data groups were divided into training set and test set according to the ratio of 7:3. Finally, on the test set, the average duration, signal-to-noise ratio (SNR) and structural similarity (SSIM) were 3.5s, 17.85 dB and 0.89 respectively.
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