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.

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...

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.

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.

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.