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In order to improve the quality of recognition of ionograms, the use of general knowledge about the reference marking of ionograms at various points of installation of ionosondes of the same type is considered. On the basis of reference markings from two ionosondes, deep neural networks were trained to highlight reflection traces from different layers of the ionosphere. The resulting deep neural networks have been successfully applied to recognize ionograms of another type of ionosonde. The results of recognition are presented.

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

Based on a new developed author’s method for recognition traces of reflections from different layers of the ionosphere in ionograms, the ionosphere parameters are extracted. The method is based on the use of deep neural networks (DNN). The rules for extracting the ionosphere parameters in ionograms are given. Based on the results obtained by the authors, an intelligent support system for ionogram analysis is being developed.

When we analyze geophysical data, the task of searching for structures by their samples and mathematical models often appears. We propose to use deep neural networks (DNN) to search and detect the forms of geophysical structures. At the same time, both the structure samples themselves and the synthesized structure samples according to their mathematical models act as a training dataset. End-to-end demonstration examples of the highlighting of reflection traces from different layers of the ionosphere in the ionograms, as well as the highlighting of whistler forms in the VLF spectrograms are presented.

Algorithms for streaming whistler recognition are offered. Different stages of algorithms are considered. The developed algorithms are used on a mini-computer software and hardware complexes for monitoring very low-frequency electromagnetic radiation at the Karymshina station in Kamchatka, on Oybenkel Geophysical Observatory of the Yu.G. Shafer Institute of Cosmophysical Research and Aeronomy (the Sakha Republic (Yakutia)), as well as recognition of whistlers is performed on the basis of open access data of abelian.org VLF station network in Todmorden United Kingdom (53,703N, 2.072W), Bielefeld Germany (52.146N, 8.458E), Cumiana Italy (44.96N, 7.42E), Warsaw Poland (52.16313N, 21.03094E), Australia Heathcote (36.804163S 144.67559E). Our whistler recognition results allowed us to establish on the days of strong whistler activity, there is an average positive correlation between the number of whistlers registered at Karymshino station in minutes of a day and the number of lightning strokes registered by WWLLN global network in minutes of a day in the coordinate rectangle LAT 25S-45S, LON 140E-160E (Australia).

Since from November 2017 to August 2018, synchronous registration of atmospherics and whistlers at the radiophysical station “Oibenkyol”, began in the operational mode within the network SSAN/VLF (“Sensor signal analysis network/Very low frequency”). The classification and the characteristics of the registered whistlers are carried out. The correlation analysis of the number of the whistlers registered by the SSAN/VLF with the number of lightning registered by the WWLLN is used to determine the location of lightning sources of whistlers in the opposite hemisphere in the magnetoconjugate point at South of Australia. Thus, the SSAN/VLF make possible the distance monitoring of the dynamics of the various geophysical processes changes. The large number of registered nose whistlers allow performing the statistical analysis of processes occurring in the magnetosphere’ plasma.

Optimization problems of construction, development and changing the structure of geospace self-organizing question-answering sensor networks (GSQASN) are considered. The task specifies the coordinates of various network nodes. It is required with the specified functional, structural, cost and spatial constraints to change the structure of the GSQASN by adding new nodes, moving to new positions or removing some existing nodes. After the formation of the GSQASN structure we solve the task of question-answer agents placement into GSQASN structure in order to be able to answer the given types of questions under the established limitations. The functional scheme of a given category nodes placement into GSQASN structure and approximate bio-inspired algorithms for solving the tasks are proposed. The results of the work can be used in the construction of specific GSQASN and in the GSQASN design support systems.

The paper considers a developing software-hardware complex «Sensor signal analysis network» for distributed and time synchronized analysis of electromagnetic radiations. The areas of application and the main features of the complex are described. An example of application of the complex to monitor natural electromagnetic radiation sources is considered based on the data recorded in VLF range. A generalized functional scheme of stream analysis of signals by a complex functional node is suggested and its application for stream detection of atmospherics, whistlers and tweaks is considered.