Using machine learning technologies to solve the problem of classifying infrasound background monitoring signals

It is widely known that among sound signals generated by natural and anthropogenic phenomena, the most long-lived are waves of frequency less than 20 Hz, called infrasound. This property allows tracking at a distance by infrasound monitoring the occurrence of high-energy events on regional scales (up to 200–300 km). At the same time, the separation of useful infrasound signals from background noise is a non-trivial task in real-time and post-facto signal processing. In this paper we propose a new method for classification of specific signals in infrasound monitoring data using Shannon permutation entropy and vectors of frequency distribution of occurrence frequencies of permutations of consecutive sample values of rank 3 (number of permutation elements). To evaluate the validity of the proposed entropy-based classification method, two machine learning methods — random forest method and classical neural network approach — implemented in Python language using Scikit-lean, TensorFlow and Keras libraries were used. The classification quality was evaluated against the traditional frequency-based method of class extraction based on Fourier transform. Recognition was performed on the prepared infrasound monitoring data in the Altai Republic. The results of computational experiment on the separation of 5 classes of signals showed that classification by the proposed method gives the same results of recognition by neural network with in comparison with frequency classification of the original data; the recognition accuracy was 51–58 %. For the random forests method, the recognition accuracy of frequency classes was slightly higher: 51 % vs. 45 % for classes using the permutation entropy method. The analysis of the results of the computational experiment shows sufficient competitiveness of the method of classification by permutation entropy in the recognition of infrasound signals. In addition, the proposed method is much easier to implement for inline signal processing in lowconsumption microcontroller systems. The next step is to test the method at infrasound signal registration points and as part of the infrasound monitoring data processing system for real-time event detection

Vector Overhauser magnetometer POS-4: experience and prospects of application

The results of practical use of a POS-4 vector magnetometer, developed by the Research Laboratory of Quantum Magnetometry, UrFU (Yekaterinburg) and based on POS Overhauser sensors, are presented. Continuous measurements by POS-4 have been carried out at the Paratunka observatory (IKIR FEB RAS, Kamchatka) since 2015, were done at the Saint Petersburg observatory (GC RAS / IZMIRAN SPb Branch, Leningrad Region) in 2017-2018 and have been performed at the Arti observatory (Institute of Geophysics, UB RAS, Sverdlovsk Region) since 2020. On the new high-latitude observatory White Sea (IAGA code WSE, GC RAS / MSU, Nikolai Pertsov White Sea Biological Station , Karelia), POS-4 is used as a main variometer for magnetic measurements. In April 2019, the magnetometer was successfully used for field measurements on ice during the TRANSARCTIC expedition in the Barents Sea (AARI, Roshydromet). At the beginning of 2021 IZMIRAN started testing two POS-4 magnetometers at the Moskow observatory. According to the results of field and observatory measurements it was possible to identify the advantages and disadvantages of the magnetometer and provide the information for its developers for further modernization in order to improve its efficiency and reliability. Many years of experience in POS-4 application determine the areas where its scientific and applied usage will provide important results, for example, for magnetic measurements in the Arctic regions or for monitoring of active zones around volcanoes