A Neural Network Method For The BFS Extraction

Krivosheev, A. I.; Konstantinov, Yu. A.; Криштоп, В. В.; Turov, Artem T.; Barkov, F. L.; Zhirnov, A. A.; Garin, E.O.; Pnev, Alexey B.

doi:10.1109/iclo54117.2022.9839892

Cited by 3 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another option that improves the processing quality can be the use of artificial intelligence to control the influence of the results obtained by each of the methods on the final physical quantities. We have already successfully demonstrated such an approach for a distributed fiber-optic sensor based on stimulated Brillouin scattering [21]. In this work, the neural network in the learning process determines the effect of each of the Brillouin frequency shift extraction methods on achieving the most accurate result for each Brillouin gain spectrum with certain parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Utilizing the Rayleigh backscattering of light propagating in an optical fiber, coherent optical reflectometry-CO-OTDR [4], phase-sensitive optical reflectometry-ϕ-OTDR [5][6][7][8][9], and optical frequency domain reflectometry-OFDR [10][11][12][13][14] have been proposed. In contrast to them, Brillouin and Raman reflectometry [15][16][17][18][19][20][21] use various types of inelastic scattering. Many of these technologies are adapted to perceive both static and dynamic external impacts on optical fiber, such as sound and other mechanical vibrations, temperature variations, and mechanical stress.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Another important area of research is the interaction of the described methods with artificial intelligence and machine learning algorithms [38][39][40][41][42]. We have already made several successful attempts to investigate this, which is presented in [23,43]. However, in both mentioned works, the processing was actually two-stage: first, the analytical algorithms functioned (independently), then the neural network was applied.…”

Section: Discussionmentioning

confidence: 99%

A Refinement of Backward Correlation Technique for Precise Brillouin Frequency Shift Extraction

Barkov

Krivosheev

Konstantinov

et al. 2023

Fibers

View full text Add to dashboard Cite

A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve fitting (LCF) based on Levenberg–Marquardt (LM) method, are studied. The effectiveness of the new method, and its combination with LM, has been demonstrated for processing spectra with a low signal-to-noise ratio (SNR). The experiments, which were in good agreement with the performed simulation, showed that at SNR = 0 dB, the combined (MBWC + LM) method provided the BFS extraction error of less than 4 MHz, while the state-of-the-art LM algorithm extracted it with the error greater than 4.5 MHz. The advantage of correlation methods becomes more significant with the decreasing SNR: at SNR = −2 dB, the LM’s error is 14.3 MHz, and that of the combined one is 8.1 MHz.

show abstract

“…Thus, it will be shown that their productive variation ensures the achievement of the maximum signal-to-noise ratio. In the future, this will allow one to create a methodology for choosing the optimal algorithm for each case (our research team has already solved this problem for distributed fiber optic sensors based on the stimulated Brillouin scattering principle, where it is also possible to use simple and cheap laser sources [42][43][44]).…”

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

View full text Add to dashboard Cite

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.

show abstract

A Neural Network Method For The BFS Extraction

Cited by 3 publications

References 7 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

A Refinement of Backward Correlation Technique for Precise Brillouin Frequency Shift Extraction

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

Contact Info

Product

Resources

About