Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition

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...

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Published in:Applied sciences 2023-02, Vol.13 (4), p.2496
Main Authors: Agayan, Sergey M., Dzeboev, Boris A., Bogoutdinov, Shamil R., Belov, Ivan O., Dzeranov, Boris V., Kamaev, Dmitriy A.
Format: Article
Language:English
Subjects:
Algorithms
California
Clustering
connectivity
density
DPS
earthquake-prone areas
Earthquakes
FCAZ
finite metric spaces
Methods
Pattern recognition
Recognition
Russia
Seismic activity
Seismicity
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container_title Applied sciences
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description 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.
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subjects Algorithms
California
Clustering
connectivity
density
DPS
earthquake-prone areas
Earthquakes
FCAZ
finite metric spaces
Methods
Pattern recognition
Recognition
Russia
Seismic activity
Seismicity
title Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition
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