## 4 Discussion  
The noise reduction algorithms studied herein and applied to DAS data processing have similar computational times. The use of any of them makes it possible to achieve output DAS data characteristics from the use of a simple and affordable homemade laser similar to those of the commercial laser source. This provides an opportunity to make distributed acoustic monitoring less expensive and, therefore, more accessible. To prove this statement, an extreme noise data filtering case was considered. Homemade laser-obtained raw data were resampled. Thus, an ADC sampling rate of 1.25 MHz instead of 200 MHz was simulated. Next, a random noise was added to it, and the resulting dataset (Figure 9a) was processed by the AFDA algorithm. The algorithm kept working  
Figure 8: Data (**a–f**) obtained using the commercial laser (CL); (**g–l**) obtained using the homemade laser—HL; (**a,g**) without filtering; (**b,h**) filtered with AFDA; (**c,e,i,k**) filtered with MA-MD; (**d,f,i,l**) filtered with MA-MD and AFDA. (**c,d,i,j**) MA-MD was adjusted to a 500 Hz frequency band; (**e,f,k,l**) MA-MD was adjusted to an 8 kHz frequency band. even in these difficult conditions and provided an SNR increase of 2.6 dB (Figure 9b), which made it possible to distinguish the signal from the noise. The study results show that the algorithms have different efficiency levels for different event frequencies. Also, some restrictions on the types of processed signals follow from the very nature of these algorithms. In addition, the time spent on post-processing can be quite a critical parameter in some applications. Thus, there is a need to differentiate algorithms according to the types and frequencies of processed events in order to reduce the calculation time and maintain an acceptable quality of the output data. MA-MD is best suited for single signals or far-spaced multiple frequency bands; provided that the event frequency is approximately known in advance, the resulting loss of data portion at the end of each time sequence is not significant, and the frequency band for which SNR is increased, but does not exceed 1/2 of the sensor sampling frequency. AFDA, in turn, is most efficient when processing wide-band signals, as well as single useful signals which are randomly distributed over the entire spectrum or in the presence of multiple harmonics. Its works adequately as long as the useful signal fraction in the data is less than the noise fraction over the total occupied spatial or frequency domain. It does not require information about the expected event frequency, although data on the approximate magnitude and width (in frequency and spatial domains) of the most significant signal components may be needed for the best performance. It is inferior to MA-MD when processing single signals from a known frequency band in the low-frequency region (relative to the sampling frequency). However, it is a good complement to MA-MD in cases in which computation time is not the most important parameter. A raw data set consisting of 7,560,000 samples was processed by the MA-MD, AFDA, and FDDA algorithms in turn. In each case, the computation time was recorded to evaluate the computational complexity. The results are presented in Table 2.  
The use of AFDA method avoids spatial distortions caused by FDDA's edge effects. The maximum size of such distortions is half the maximum length of the scanning window. The AFDA method does not provide for edge effects, so the spatial resolution when using it is determined only by the length of the probing pulse and the sampling frequency of the data acquisition card. Thus, for a scanning window length of 200 m, the spatial resolution when using the FDDA method is 100 m, and when using the AFDA method it is 10 m, since the pulse length is 100 ns (Table 2). The MA-MD computation time, of course, slightly depends on the MA and MD window sizes. Its value, shown in Table 2, was achieved with a window size of 1.