On one approach to the detection of infrasonic signals of irregular natural phenomena in the instrumental observations time series at the student interdisciplinary testing ground

Institute of Physics & Mathematics and Engineering & Technology, Gorno-Altaisk State University (GASU), 649000 Gorno-Altaisk, Russia

^* Corresponding author: ngkudr@mail.ru

Abstract

The work is devoted to the analysis of time series and the problem of processing signals obtained as a result of the design approach implementation during the organization of instrumental observations of irregular natural phenomena at the student interdisciplinary testing ground. The objective of the work is to study the methods of processing noisy signals obtained as a result of monitoring the infrasonic environment, which make it possible to automate the search for fragments of the time series generated by irregular natural phenomena. At the beginning of the work, a brief explanation of the essence of the measuring scientific experiment carried out within the framework of the project approach used in the additional education of students and schoolchildren shall be given. The following is a review of publications describing various approaches to the analysis of nonstationary time series obtained in the process of instrumental observations. As the main method of time series analysis, it is proposed to use the algorithm for calculating the fractal dimension of the time series, proposed by T. Higuchi [1]. During studying of the time series of infrasonic signals, a number of regularities were discovered that contribute to the development of an original procedure for processing and transforming the signal under study, which makes it possible to determine the time intervals of fragments of the time series corresponding to the signals of the desired natural phenomena. The essence of the proposed approach lies in the preliminary preparation of the time series by processing the data with a simple normalized difference filter, previously smoothed by performing the coenvolution (convolution) operation with a Gaussian kernel; determining the step of segmenting the normalized time series, calculating fractal dimensions and averaged amplitudes for each of the segments of the time series and obtaining on their basis vectors of changes in dimensions and amplitudes with their subsequent element-wise multiplication. It is shown that the maximum values of the components of the resulting vector are indicators of timestamps for the location of the desired signals.