## 1 Introduction  
Fiber-optic distributed acoustic sensors (DAS) based on optical time-domain reflectometry (OTDR) technology were proposed more than 40 years ago [1]. Their development is directly related to approximately simultaneous creation of low loss optical fiber [2] and an OTDR itself as their manufacturing quality control method [3]. Since then, this area has grown very rapidly. Utilizing the Rayleigh backscattering of light propagating in an optical fiber, coherent optical reflectometry--CO-OTDR [4], phase-sensitive optical reflectometry--\(\varphi\)-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. Over the last few decades, acoustic monitoring has gained great popularity in many industries such as oil and gas production [22], processing [23], transportation [24], geophysics and mineral deposits exploration [25], protection of territories [26], and structural integrity monitoring of vital engineering constructions [27; 28; 29]. At the moment, such monitoring is usually performed by fiber optic DASs (due to their intrinsic advantages) using phase-sensitive technology (\(\varphi\)-DAS) implemented with the use of high-coherence light sources and direct or hybrid backscattering detection, since conventional OTDR is not sensitive to dynamic deformations, has a higher noise level, and requires additional time for the obtained data averaging. Moreover, its event response time is generally worse. At the same time, the necessity to use a highly coherent radiation source, a high-speed optical modulator [30], a photodetector, and ADC, as well as several optical amplifiers in DAS makes them rather expensive. Therefore, such systems are widespread only in top-financed areas of science and technology. However, state-of-the-art studies are more and more often dedicated to fiber optic acoustic sensors applications on behalf of less funded industries; for example, agriculture [31] and ecology [32; 33]. It is reported that meteorology and biology [34; 35] possess the need for a distributed acoustic sensor with sensitivity to acoustic events with certain frequencies. Thus, a sensor capable of satisfying such requests must have the most optimal price-performance ratio. This can be achieved by cost reduction, which can result from excluding some expensive setup elements and by bringing algorithms to the forefront instead of hardware where possible. The state-of-the-art concept of processing distributed fiber-optic sensor signals mainly provides for automatic events classification occurring at a certain distance from the point where radiation is injected into the sensor. For this, specially created databases, correlation algorithms, neural networks, empirical mode decomposition, and variational mode decomposition are used [36; 37; 38; 39]. In fact, the main idea of these studies is to decompose the signal not into the frequency spectrum using FFT, but into some modes that describe one or another physical process. In the pioneering work of [38], the variational mode decomposition technique for threat classification in phase-OTDR-based DAS systems was studied for the first time. To perform the high-precise processing, the Wavelet filtering technique was used before the decomposition, for the denoising of the raw data, and then high-pass filtering was applied. Another stage of denoising was the application of the autocorrelation function. The correlation peaks were interpreted as events, and only after that, the variational mode decomposition technique was applied to classify different impacts on the sensor.