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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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2496 |
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| author | Sergey M. Agayan Boris A. Dzeboev Shamil R. Bogoutdinov Ivan O. Belov Boris V. Dzeranov Dmitriy A. Kamaev |
| author_facet | Sergey M. Agayan Boris A. Dzeboev Shamil R. Bogoutdinov Ivan O. Belov Boris V. Dzeranov Dmitriy A. Kamaev |
| author_sort | Sergey M. Agayan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T09:12:14Z |
| format | Article |
| id | doaj.art-9bfd7fe0eef14f3b881b5fbe5243011d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:12:14Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9bfd7fe0eef14f3b881b5fbe5243011d2023-11-16T18:56:44ZengMDPI AGApplied Sciences2076-34172023-02-01134249610.3390/app13042496Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area RecognitionSergey M. Agayan0Boris A. Dzeboev1Shamil R. Bogoutdinov2Ivan O. Belov3Boris V. Dzeranov4Dmitriy A. Kamaev5Geophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaGeophysical Center of the Russian Academy of Sciences, 119296 Moscow, RussiaResearch and Production Association “Typhoon”, 249038 Obninsk, RussiaThe 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.https://www.mdpi.com/2076-3417/13/4/2496FCAZDPSearthquake-prone areasdensityfinite metric spacesconnectivity |
| spellingShingle | Sergey M. Agayan Boris A. Dzeboev Shamil R. Bogoutdinov Ivan O. Belov Boris V. Dzeranov Dmitriy A. Kamaev Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition Applied Sciences FCAZ DPS earthquake-prone areas density finite metric spaces connectivity |
| title | Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition |
| title_full | Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition |
| title_fullStr | Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition |
| title_full_unstemmed | Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition |
| title_short | Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition |
| title_sort | development of the algorithmic basis of the fcaz method for earthquake prone area recognition |
| topic | FCAZ DPS earthquake-prone areas density finite metric spaces connectivity |
| url | https://www.mdpi.com/2076-3417/13/4/2496 |
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