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: Fiber Bragg gratings, Denoising, Sensing systems, Signal to noise ratio, Wavelets, Wavelength division multiplexing, Time division multiplexing, Temperature metrology, Random lasers, Light
In this paper, a novel fiber Bragg grating (FBG) sensing system is proposed with large capacity and long transmission
distance to achieve multi-parameter measurements. Record system performances are achieved via the use of high-order
random lasing and remote optical pumping amplifications as well as the combination of time-division multiplexing and
wavelength-division multiplexing technologies. The experimental results show that the sensing distance can reach 150km
with single-end amplification and the optical signal-to-noise ratio (OSNR) is >4dB with good linearity of 0.9992 for 308
FBGs. We also proposed a new denoising method based on deep-learning, and the OSNR is enhanced to 10.2dB from
4.1dB, which is much better than the wavelet and empirical mode decomposition (EMD) methods reported, ensuring the
high accuracy of the center wavelength detection with deep-learning denoising correspondingly.
Fiber-optic distributed acoustic sensing (DAS) technology has been extensively applied in many different fields, while enhancement of its signal-to-noise ratio (SNR) is always of the first priority to let its high sensitive perception ability be brought into full play. In this paper, a novel DAS signal denoising methods is proposed by utilizing the adaptive beamforming (ABF) of its array signal rather than a single point signal, for the first time. Three ABF algorithms are comparatively studied, including minimum variance (MV), eigenspace-based minimum-variance (ESBMV), and coherence factor (CF) filtering. The experimental results show that these three algorithms improve the DAS signals by a similar level of 10.6 dB, 10.6 dB, and 10.2 dB, respectively for noisy DAS signal with SNR of 29.2 dB. The processing time for the three methods is also compared, and it shows that the MV takes the shortest time of only 11 ms, which is the most promising ABF denoising method for DAS. It is highly anticipated that this ABF method could be used in high-performance DAS systems for applications in oil/gas exploration, seismic surveillance, pipeline monitoring, and submarine acoustic detection, et al.
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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