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.

Modern satellite positioning and navigation technologies are not applicable in specific areas such as the exploration of oil and gas deposits by means of directional drilling techniques. Here, we can rely solely on natural geophysical fields, such as the Earth’s magnetic field. The precise underground navigation of borehole drilling instruments requires a seamless, near-real-time access to operational geomagnetic data. This paper describes the MAGNUS BD hardware-software system, deployed at the Geophysical Center of the Russian Academy of Sciences, that provides the efficient accumulation, storage, and processing of geomagnetic data. This system, based on the Big Data (BD) technology, is a modern successor of the MAGNUS processing software complex developed in 2016. MAGNUS BD represents one of the first cases of the BD technology’s application to geomagnetic data. Its implementation provided a significant increase in the speed of information processing and allowed for the use of high-frequency geomagnetic satellite data and expanding the overall functionality of the system. During the MAGNUS BD system’s deployment on a physically separate dedicated cluster, the existing classical database (DB) was migrated to the Arenadata database with full preservation of its functionality. This paper gives a brief analysis of the current problems of directional drilling geomagnetic support and outlines the possible solutions using the MAGNUS BD system.

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.

The paper describes the course of the COVID-19 pandemic using a combination of mathematical statistics and discrete mathematical analysis (DMA) methods. The method of regression derivatives and FCARS algorithm as components of DMA will be for the first time tested outside of geophysics problems. The algorithm is applied to time series of the number of new cases of COVID-19 infections per day for some regions of Russia and the Republic of Austria. This allowed to assess the nature and anomalies of pandemic spread as well as restrictive measures and decisions taken in terms of the administration of countries and territories. It was shown that these methods can be used to identify time intervals of change in the nature of the incidence rate and areas with the most severe course of the epidemic. This made it possible to identify the most significant restrictive measures that allowed to reduce the growth of the disease.

The present paper continues the series of publications by the authors devoted to solving the problem of recognition regions with potential high seismicity. It is aimed at the development of the mathematical apparatus and the algorithmic base of the FCAZ method, designed for effective recognition of earthquake-prone areas. A detailed description of both the mathematical algorithms included in the FCAZ in its original form and those developed in this paper is given. Using California as an example, it is shown that a significantly developed algorithmic FCAZ base makes it possible to increase the reliability and accuracy of FCAZ recognition. In particular, a number of small zones located at a fairly small distance from each other but having a close “internal” connection are being connected into single large, high-seismicity areas.

In this study, we developed a new approach for feature engineering in geosciences. The main focus of this study was feature engineering based on the implementation of the dynamic activity index (MDAI) as a function of the anomaly of the spatial distribution of data, using systems and discrete mathematical analysis. The methodology for calculating MDAI by groups, geomorphological variability, the density of tectonic faults, stress-strain state, and magnetic field anomalies, is presented herein for a specific area. A detailed analysis of the correlation matrix of MDAI revealed weak correlations between the development features. This showed that the considered properties of the geological environment are independent sets and can be used in the analysis of its geodynamic stability. As a result, it was found that most of the territory where high-level radioactive waste (HLRW) disposal is currently planned is in a relatively stable zone.

The article is devoted to earthquake-prone areas recognition with M ≥ 6.0 in the Caucasus and in the Altai–Sayan–Baikal region. A new approach to the classification of intersections of morphostructural lineaments using the definition of a fuzzy set is proposed. The latter enables an integral interpretation of a single result (composition) of high seismicity zones recognition performed by the Barrier-3 and Kora-3 algorithms.

Typically, strong earthquakes do not occur over the entire territory of the seismically active region. Recognition of areas where they may occur is a critical step in seismic hazard assessment studies. For half a century, the Earthquake-Prone Areas (EPA) approach, developed by the famous Soviet academicians I.M. Gelfand and V.I. Keilis-Borok, was used to recognize areas prone to strong earthquakes. For the modern development of ideas that form the basis of the EPA method, new mathematical methods of pattern recognition are proposed. They were developed by the authors to overcome the difficulties that arise today when using the EPA approach in its classic version. So, firstly, a scheme for the recognition of high seismicity disjunctive nodes and the vicinities of axis intersections of the morphostructural lineaments was created with only one high seismicity learning class. Secondly, the system-analytical method FCAZ (Formalized Clustering and Zoning) has been developed. It uses the epicenters of fairly weak earthquakes as recognition objects. This makes it possible to develop the recognition result of areas prone to strong earthquakes after the appearance of epicenters of new weak earthquakes and, thereby, to repeatedly correct the results over time. It is shown that the creation of the FCAZ method for the first time made it possible to consider the classical problem of earthquake-prone areas recognition from the point of view of advanced systems analysis. The new mathematical recognition methods proposed in the article have made it possible to successfully identify earthquake-prone areas on the continents of North and South America, Eurasia, and in the subduction zones of the Pacific Rim.

The paper presents the structure of a new original FDPS (Functional Discrete Perfect Sets) algorithm used to filter and arrange the layers of the geospatial data into the homogenous groups and identify dense homogenous condensations. The latter may be related to the deep zones of dynamic instability in the upper part of the Earth's crust. Synthetic and real examples of this algorithm's usage are presented, demonstrating its capabilities as part of the system analysis of the geological environment stability in the area of construction of a deep disposal site for high-level radioactive waste. Testing the algorithm allowed us to identify the most stable blocks, thereby demonstrating its usage value. This shows the necessity of further development and use of the FDPS algorithm.

A new version of the Barrier algorithm is proposed for recognition of strong-earthquake prone regions based on training over a single reliable training class. The modification of the algorithm consists in creating blocks that reveal the geological–geophysical features (attributes) characteristic of the recognized highly seismic objects and provide their quantitative estimates. The recognition of the areas prone to earthquakes with M ≥ 6.0 is carried out for the Altai–Sayan–Baikal region. The results of the recognition are used for assessing the effect of the remote earthquakes that occurred in the Altai–Sayan orogenic region on the stability of structural-tectonic crustal blocks in the contact zone of the West Siberian platform and the Siberian plate.

В работе предложена новая версия алгоритма «Барьер» для распознавания мест возможного возникновения сильных землетрясений на основе обучения по единственному достоверному классу обучения. Модификация алгоритма заключается в создании блоков, которые позволяют установить геолого-геофизические признаки, свойственные распознанным высокосейсмичным объектам распознавания, и дать их количественную оценку. Выполнено распознавание мест возможного возникновения землетрясений с М ≥ 6.0 в регионе Алтай–Саяны–Прибайкалье. Результаты распознавания использованы для оценки влияния удаленных землетрясений, произошедших в Алтае-Саянской орогенной области, на устойчивость структурно-тектонических блоков земной коры в зоне контакта Западно-Сибирской платформы и Сибирской плиты.

(1) Geophysical Center of the Russian Academy of Sciences (GC RAS), Moscow, Russian Federation (b.dzeboev@gcras.ru), (2) Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences (IPE RAS), Moscow, Russian Federation (direction@ifz.ru), (3) Russian State Geological Prospecting University n. a. Sergo Ordzhonikidz (MGRI-RSGPU), Moscow, Russian Federation (foreignmsgpa@mail.ru), (4) Geophysical Institute the Affiliate of Vladikavkaz Scientific Centre of the Russian Academy of Sciences (GPI VSC RAS), Vladikavkaz, Russian Federation (cgi_ras@mail.ru)

(1) Geophysical Center of the Russian Academy of Sciences (GC RAS), Moscow, Russian Federation (b.dzeboev@gcras.ru), (2) Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences (IPE RAS), Moscow, Russian Federation (direction@ifz.ru), (3) Russian State Geological Prospecting University n. a. Sergo Ordzhonikidz (MGRI-RSGPU), Moscow, Russian Federation (foreignmsgpa@mail.ru), (4) Geophysical Institute the Affiliate of Vladikavkaz Scientific Centre of the Russian Academy of Sciences (GPI VSC RAS), Vladikavkaz, Russian Federation (cgi_ras@mail.ru)

This paper continues the series of our works on recognizing the areas prone to the strongest, strong, and significant earthquakes in different mountain countries with the use of the algorithmic Formalized Clustering And Zoning (FCAZ) system. In the paper, for the first time from this standpoint, we study the joint Altai–Sayan region, in which areas prone to significant earthquakes are recognized. A geological description of the concerned region is presented.

This report continues a series of works by the authors on earthquake-prone areas recognition by the algorithmic system FCAZ. For the first time, successive earthquake-prone areas recognition for several magnitude thresholds in the same seismic region is conducted. This can be done by iteratively narrowing the set of recognition objects of the FCAZ system. Earthquake-prone areas for a given magnitude threshold are recognized within zones already recognized as dangerous for a smaller threshold magnitude. The reproducibility of the study is ensured by the fact that at all stages the recognition algorithm remains unchanged. Earthquakeprone areas with magnitude thresholds of М ≥ 5.5, М ≥ 5.75, and М ≥ 6.0 in the Baikal–Transbaikal region are studied successively.

Сообщение продолжает цикл работ авторов по распознаванию мест возможного возникновения землетрясений с помощью алгоритмической системы FCAZ. Впервые проводится последовательное распознавание мест возможного возникновения эпицентров землетрясений для нескольких магнитудных порогов в одном и том же сейсмоопасном регионе. Это удаётся сделать за счёт итерационного сужения множества объектов распознавания системы FCAZ. Зоны возможного возникновения эпицентров землетрясений для данного магнитудного порога распознаются внутри зон уже распознанных как опасные для меньшей пороговой магнитуды. Воспроизводимость исследования обеспечивается тем, что на всех этапах алгоритм распознавания остаётся неизменным. Последовательно изучаются места возможного возникновения эпицентров землетрясений с магнитудой М 5,5, М 5,75 и М 6,0 в регионе Прибайкалье-Забайкалье.

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one “pure” high-seismic class. The new algorithm operates in the space of absolute values of the geological–geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with М ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.