Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors
Artem T. Turov,
Fedor L. Barkov,
Yuri A. Konstantinov,
Dmitry A. Korobko,
Cesar A. Lopez-Mercado,
Andrei A. Fotiadi
Affiliations
Artem T. Turov
General Physics Department, Applied Mathematics and Mechanics Faculty, Perm National Research Polytechnic University, Prospekt Komsomolsky 29, 614990 Perm, Russia
Fedor L. Barkov
Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenin St., 614000 Perm, Russia
Yuri A. Konstantinov
Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenin St., 614000 Perm, Russia
Dmitry A. Korobko
S.P. Kapitsa Research Institute of Technology, Ulyanovsk State University, 42 Leo Tolstoy Street, 432970 Ulyanovsk, Russia
Cesar A. Lopez-Mercado
Scientific Research and Advanced Studies Center of Ensenada (CICESE), Ensenada 22860, BC, Mexico
Andrei A. Fotiadi
Electromagnetism and Telecommunication Department, University of Mons, B-7000 Mons, Belgium
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.
