High-Performance Distributed Acoustic Sensing with Coherent Detection

Wang, Yuyao; Xu, Ruijuan; Deng, Ziwen; Liang, Yongxin; Jiang, Jialin; Wang, Zinan

doi:10.1109/icicn56848.2022.10006550

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Lastly, the MD method can be applied in the frequency domain, just like FDDA, and the neural network will determine their contribution to the detection accuracy of the required frequency with maximum SNR. While using software methods to improve signal quality, one should not forget about the possibilities of hardware modification of the optoelectronic system [58][59][60][61][62][63] and sensor fiber or cable [57,[64][65][66][67][68][69]. We believe that these works will be the priority of our scientific group in the future.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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Moving differential and dynamic window moving averaging are simple and well-known signal processing algorithms. However, the most common methods of obtaining sufficient signal-to-noise ratios in distributed acoustic sensing use expensive and precise equipment such as laser sources, photoreceivers, etc., and neural network postprocessing, which results in an unacceptable price of an acoustic monitoring system for potential customers. This paper presents the distributed fiber-optic acoustic sensors data processing and noise suppression techniques applied both to raw data (spatial and temporal amplitude distributions) and to spectra obtained after the Fourier transform. The performance of algorithms’ individual parts in processing distributed acoustic sensor’s data obtained in laboratory conditions for an optical fiber subjected to various dynamic impact events is studied. A comparative analysis of these parts’ efficiency was carried out, and for each type of impact event, the most beneficial combinations were identified. The feasibility of existing noise reduction techniques performance improvement is proposed and tested. Presented algorithms are undemanding for computation resources and provide the signal-to-noise ratio enhancement of up to 13.1 dB. Thus, they can be useful in areas requiring the distributed acoustic monitoring systems’ cost reduction as maintaining acceptable performance while allowing the use of cheaper hardware.

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Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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“…Distributed fiber optic sensors capable of recording events at a high frequency are of particular interest. As the frequencies detected by the instruments of this type in most cases turn out to be acoustic, such distributed sensors have also become known as acoustic (Distributed Acoustic Sensors-DAS) [5][6][7][8][9][10][11][12]. Since they make it possible to register the optical fiber longitudinal deformation changes at each point with frequencies from units of hertz to tens of kilohertz, it is not surprising that their use was not limited to the quite obvious functions of protection of perimeters, pipelines, and critical infrastructure facilities, but began to develop towards metrology and flaw detection.…”

Section: Introductionmentioning

confidence: 99%

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Turov,

Barkov,

Konstantinov

et al. 2023

Algorithms

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This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms’ combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS.

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High-Performance Distributed Acoustic Sensing with Coherent Detection

Cited by 2 publications

References 32 publications

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

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