Laboratory of Geoinformatics and Geomagnetic Research

Soloviev A.A., Belov I.O., Vorobev A.V., Sergeev V.N.

In this study, we consider historical geomagnetic satellite data obtained during a strong magnetic storm on March 8−9, 1970. In addition to the data of the Soviet satellite Kosmos-321, data from the American satellite OGO-6, which performed geomagnetic measurements at the same time, were used. We analyzed time variations of external magnetic fields recorded in satellite and ground-based observations of the magnetic field. The research also gave impetus to the creation of the improved software implementation of the auroral oval model APM, which enables reconstruction of its position and precipitation intensity in both the past and near real time. The magnetic variations originating in the near-Earth space from various sources were identified. In particular, we revealed the signatures of the storm-time ring current and equatorial and auroral electrojects. The paper highlights the enduring value of historical data of magnetic field observations stored in data centers and continuously digitized by their staff.

Getmanov V., Gvishiani A., Soloviev A., Zajtsev K., Dunaev M., Ehlakov E.

We solve the problem of recognizing geomagnetic storms from matrix time series of observations with the URAGAN muon hodoscope, using deep learning neural networks. A variant of the neural network software module is selected and its parameters are determined. Geomagnetic storms are recognized using binary classification procedures; a decision-making rule is formed. We estimate probabilities of correct and false recognitions. The recognition of geomagnetic storms is experimentally studied; for the assigned Dst threshold Yᴅ₀=–45 nT we obtain acceptable probabilities of correct and false recognitions, which amount to β=0.8212 and α=0.0047. We confirm the effectiveness and prospects of the proposed neural network approach.

Getmanov V., Gvishiani A., Soloviev A., Zajtsev K., Dunaev M., Ehlakov E.

We solve the problem of recognizing geomagnetic storms from matrix time series of observations with the URAGAN muon hodoscope, using deep learning neural networks. A variant of the neural network software module is selected and its parameters are determined. Geomagnetic storms are recognized using binary classification procedures; a decision-making rule is formed. We estimate probabilities of correct and false recognitions. The recognition of geomagnetic storms is experimentally studied; for the assigned Dst threshold Yᴅ₀=–45 nT we obtain acceptable probabilities of correct and false recognitions, which amount to β=0.8212 and α=0.0047. We confirm the effectiveness and prospects of the proposed neural network approach.

Agayan S., Bogoutdinov S., Sidorov R., Soloviev A., Kamaev D., Aleksanyan A., Dzeranov B.

Discrete Mathematical Analysis (DMA) is a data analysis method that uses fuzzy mathematics and fuzzy logic. DMA involves the active participation of the researcher in the study of records, offering technologies and algorithms for analyzing records through the properties of interest to the researcher. In the present work, such properties are related to regression derivatives, and the results obtained are applied to magnetic records. The possibilities of the method in the morphological analysis of geomagnetic storms are demonstrated on the example of three strongest storms that have occurred since the beginning of the current 25th solar cycle.

Belov I., Soloviev A., Pilipenko V., Dobrovolskiy M., Bogoutdinov S., Kalinkin K.

In this paper, we describe the TeslaSwarm online system [http://aleph.gcras.ru/teslaswarm] for visualizing field-aligned currents in the upper ionosphere, using data from Swarm low-orbit satellites. The system provides researchers with a simple and convenient tool for event selection and detailed analysis of currents and electromagnetic fields in the upper ionosphere. The system user can select satellite passages over a given region, visualize the geomagnetic field structure and field-aligned currents, compare the pattern of field-aligned currents with the auroral particle precipitation map, using the OVATION-Prime model, and save the selected parameters in a file in text format. We demonstrate advantages of the developed system over its foreign analogues. In practice, the collection and pre-processing of raw data for experiments make up about 80 % of all work with data. The proposed online system largely saves the user from the most time-consuming work of choosing the required satellite passage segments and calculating the characteristics of interest from raw measurements.

Gvishiani A., Rozenberg I., Soloviev A., Krasnoperov R., Shevaldysheva O., Kostianoy A., Lebedev S., Dubchak I., Sazonov N., Nikitina I., Gvozdik S., Sergeev V., Gvozdik G.

Arctic zone of the Russian Federation (AZRF) is the region of intensive economic development. In this regard, it is critical to give an adequate assessment of natural factors that may have a negative impact on the growing technological infrastructure. Rapid climate change effects show a significant influence on this activity, including the railway network development. Hence, the decision-making community requires relevant information on climatic variations that can put at hazard the construction and operation of railway facilities. This paper presents the analysis of climatic changes within the region of Central and Western Russian Arctic in 1980–2021. It was performed using the new electronic Atlas of climatic variations in main hydrometeorological parameters, created for the Russian Railways in 2023. This geoinformatic product includes about 400 digital maps reflecting the variability of seven climatic parameters over more than four decades within the studied region. These parameters are air temperature, total precipitation, wind speed, soil temperature, soil moisture content, air humidity, and snow cover thickness. The analysis of climatic maps and their comparison between selected periods showed spatial and temporal heterogeneity of climatic variations in this region. This justifies the feasibility of further research using additional analytical instruments, such as Hovmöller diagrams, time series graphs, etc. The implementation of advanced geoinformatic products in the practice of the Russian Railways will facilitate sustainable development of its infrastructure in rapidly altering climatic conditions.

Soloviev A.A.

Abstract —Continuous growth of geomagnetic observations requires adequate methods for their processing and analysis. However, many studies in the field of geomagnetism require accurate and reliable measurements performed on the ground and in outer space. Mathematical methods of geoinformatics can provide a solution to these problems. The article describes the progress made in the field of intellectual analysis of geomagnetic data continuously recorded by magnetic observatories and low-orbit satellites, demonstrates the results in the study of rapid variations of the Earth’s core magnetic field associated with the deep processes of the Earth, and presents the investigation of near-Earth electromagnetic dynamics. The applied value of the obtained results is shown.

Soloviev A.A.

This paper is focused on study of the geomagnetic field variation response caused by a series of earthquakes with magnitudes of Mw = 7.5–7.8 in Turkey on February 6, 2023. The initial data include high-precision observations of the geomagnetic field with a one-second sampling rate recorded at magnetic observatories in Russia and neighboring countries from middle to high latitudes. The paper analyzes the morphology of the geomagnetic signal, its amplitude-frequency characteristics, pulses in the rate of change and delays of the geomagnetic field response to earthquakes with magnitudes Mw = 7–8 depending on the distance to the source. The results suggest that the geomagnetic effect is detected best in the rate of change recordings, reaching anomalous amplitudes of 10 nT/s. The signal delay ranges from 221 to 592 s depending on the magnetic field component and the distance to the epicenter, which is in the range from 765 to 2650 km for the selected observatories.

Kudin D., Soloviev A., Matveev M., Shevaldysheva O.

High-quality geomagnetic measurements are widely used in both fundamental research of the magnetic field and numerous industrial applications. However, vector data measured by fluxgate sensors show a dependency on temperature due to sensitive coil core material and components of the sensor electronics. Here, we propose a new method for detecting and eliminating temperature dependence in magnetic observatory data. The method is designed to correct temperature drifts in variation vector magnetometer measurements when preparing quasi-definitive data according to an INTERMAGNET standard. A special feature of the method is the semi-automatic adjustment of localization intervals for temperature correction, which prevents boundary jumps and discontinuities in the course of sequential data processing over long intervals. The conservative nature of the approach implies the minimization of the original data amount subjected to correction. The described method is successfully applied in the routine monthly preparation of quasi-definitive data of the Saint Petersburg Observatory (IAGA-code SPG) and can be efficiently introduced at other magnetic observatories worldwide.

Vorobev A., Soloviev A., Pilipenko V., Vorobeva G., Gainetdinova A., Lapin A., Belahovskiy V., Roldugin A.

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved. In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship. So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function. The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone. In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Vorobev A., Soloviev A., Pilipenko V., Vorobeva G., Gainetdinova A., Lapin A., Belahovskiy V., Roldugin A.

Despite the existing variety of approaches to monitoring space weather and geophysical parameters in the auroral oval region, the issue of effective prediction and diagnostics of auroras as a special state of the upper ionosphere at high latitudes remains virtually unresolved. In this paper, we explore the possibility of local diagnostics of auroras through mining of geomagnetic data from ground-based sources. We assess the significance of indicative variables and their statistical relationship. So, for example, the application of Bayesian inference to the data from the Lovozero geophysical station for 2012–2020 has shown that the dependence of a posteriori probability of observing auroras in the optical range on the state of geomagnetic parameters is logarithmic, and the degree of its significance is inversely proportional to the discrepancy between empirical data and approximating function. The accuracy of the approach to diagnostics of aurora presence based on the random forest method is at least 86 % when using several local predictors and ~80 % when using several global geomagnetic activity indices characterizing the geomagnetic field disturbance in the auroral zone. In conclusion, we discuss promising ways to improve the quality metrics of diagnostic models and their scope.

Pilipenko V., Chernikov A., Soloviev A., Yagova N., Saharov Y., Kudin D., Kostarev D., Kozyreva O., Vorobiev A., Belov A.

The review offered for the first time in the Russian scientific literature is devoted to various aspects of the problem of the impact of space weather on ground transport systems. An analysis of available information indicates that space weather disturbances can affect rail infrastructure through both direct and indirect effects on system components. One of the main hazards is geomagnetically induced currents (GICs) in grounded extended structures excited by the geomagnetic field disturbances. The telluric electric fields and currents associated with them can cause power outages and malfunctions in the railway automation track circuits. Indirect impact is possible through disturbances in the stable supply of electricity, disturbances in communication systems and in the appearance of positioning errors in global navigation satellite systems. The review provides information necessary for engineers of transport and energy systems about the main factors of space weather that could pose a threat to such systems. Examples of the influence of geomagnetic disturbances on the automatic signaling of the northern sections of Russian Railways are given. The prospects for monitoring space weather and the aurora oval for the needs of Russian Railways are discussed.

А. Д. Гвишиани, В. Я. Панченко, И. М. Никитина

Currently, big data is one of the most discussed phenomena in the field of information technology. What is big data? How are they created? How does big data software work? How is big data theory and practice used and can be used in relation to geosciences? How is the issue of big data developing within the Russian Academy of Sciences? These are the questions discussed in an article prepared on the basis of a scientific report presented at a meeting of the Presidium of the Russian Academy of Sciences on March 28, 2023.