Strong-Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai–Sayan–Baikal Region, М ≥ 6.0

Дзебоев, Б. А.; Гвишиани, А. Д.; Belov, Ivan; Agayan, S. M.; Tatarinov, V. N.; Barykina, Yu. V.

doi:10.1134/s1069351319040050

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…The unified catalog is made available to the public at: http://www.wdcb.ru/arctic_antarctic/arctic_ seism.html, (accessed on 1 June 2023). The integrated earthquake catalog obtained in this paper is intended for a wide range of researchers involved in both the study of the seismic regime of the Arctic and, in general, the seismic hazard assessment [37][38][39][40][41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Integrated Earthquake Catalog II: The Western Sector of the Russian Arctic

Vorobieva,

Gvishiani,

Shebalin

et al. 2023

Applied Sciences

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The article is a continuation of the research on creating the most complete and representative earthquake catalogs by combining all available data from regional, national, and international seismological agencies and reducing magnitudes to a uniform scale. The task of identifying and removing duplicates that arise during the merging process is solved using the authors’ modification of the nearest neighbor method. It is evident that the intelligent merging of different earthquake catalogs for the same territory will improve the completeness and representativeness of events in the final integrated catalog. In this article, the earthquake catalog of the western sector of the Arctic zone of the Russian Federation (AZRF) covering the period 1962–2022 was created by merging three regional Russian catalogs and the ISC catalog. The ratio of magnitude types in the catalog for different seismic networks was analyzed, and magnitude estimates were unified based on the obtained ratios. For analyzing seismic activity in the western AZRF, it is recommended to use earthquakes from the period 1998–2020 when the catalog was significantly cleaned from explosions and other events of the “non-earthquake” type.

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Section: Discussionmentioning

confidence: 99%

Integrated Earthquake Catalog II: The Western Sector of the Russian Arctic

Vorobieva,

Gvishiani,

Shebalin

et al. 2023

Applied Sciences

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“…Currently, research is underway to formalize the methodology for identifying earthquake foci in different regions and to clarify lineament-domain focal models. This could be achieved by complex geological and geophysical data processing with a fuzzy logic algorithm [Dzeboev et al, 2019;Gvishiani et al, 2021;Kulchinsky et al, 2010]. Due to the fact that a significant portion of the Russian Federation's territory has not been sufficiently studied in terms of seismology, we have developed an algorithm for identifying areas that encompass homogeneous geological features and dissipated seismicity, which we call seismic generation zones here or "seismodomains", "seismic domains" in Russian [Ulomov, 1987].…”

Section: Introductionmentioning

confidence: 99%

Seismic Domain Identification Algorithm Using Fuzzy Logic Methods With Combined Geological and Geomorphological Data for the Case of Sakhalin Island

Sobisevich,

Steblov,

Agibalov

et al. 2024

Russian Journal of Earth Sciences

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An algorithm for identifying seismic generation zones or “seismic domains” using fuzzy logic has been developed and tested on the island of Sakhalin. Initial data were obtained from diagrams of the distribution of “weak” zones, relief elevation distribution skewness, and magnitude of recent area deformation for one year. These data were processed using a γ-operator in fuzzy logic with γ = 0.9, which allowed us to identify areas with high seismic activity. The areas where these active areas intersect with zones with increased compressive stress values, as determined by computer modeling, were considered to be seismic zones. It was shown that, if there are not enough source materials available, it is possible to exclude information about the recent deformation field from consideration and use an assumed grid of active faults for computer modeling. This approach may be useful when analyzing areas that have not been studied well.

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“…The feature of a study object is the result of measuring or modeling some individual property of the object, and their aggregate reflects the model of the object. The solution to urgent problems in assessing natural and man-made risks, such as searching for anomalies in geophysical fields [1][2][3], recognizing strong earthquake-prone areas [4][5][6][7][8], geodynamic zoning [9,10], etc., requires the creation of effective methods for the formalized analysis of a complex of geological and geophysical features. When analyzing the data, the features are synthesized using mathematical modeling methods [11][12][13] and may contain complex mathematical constructions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste

et al. 2022

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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.

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Strong-Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai–Sayan–Baikal Region, М ≥ 6.0

Cited by 10 publications

References 20 publications

Integrated Earthquake Catalog II: The Western Sector of the Russian Arctic

Integrated Earthquake Catalog II: The Western Sector of the Russian Arctic

Seismic Domain Identification Algorithm Using Fuzzy Logic Methods With Combined Geological and Geomorphological Data for the Case of Sakhalin Island

Dynamic Activity Index for Feature Engineering of Geodynamic Data for Safe Underground Isolation of High-Level Radioactive Waste

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