Activation function dynamic averaging as a technique for nonlinear 2D data denoising in distributed acoustic sensors
Turov, Artem T.; Barkov, Fedor L.; Konstantinov, Yuri A.; Korobko, Dmitry A.; Lopez-Mercado, Cesar A.; Fotiadi, Andrei A. (2023-09-13)
Turov, A.T.; Barkov, F.L.; Konstantinov, Y.A.; Korobko, D.A.; Lopez-Mercado, C.A.; Fotiadi, A.A. Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors. Algorithms 2023, 16, 440. https://doi.org/10.3390/a16090440
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe20231003138500
Tiivistelmä
Abstract
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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