Skip to main content
Log in

Fuzzy Sets of High Seismicity Intersections of Morphostructural Lineaments in the Caucasus and in the Altai–Sayan–Baikal Region

  • Published:
Journal of Volcanology and Seismology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Akopian S.Ts., Bondur V.G., Rogozhin E.A. Technology for monitoring and forecasting strong earthquakes in Russia with the use of the seismic entropy method // Izvestiya, Physics of the Solid Earth. 2017. Vol. 53. Is. 1. P. 32–51. https://doi.org/10.1134/S1069351317010025

  2. Alekseevskaya, M., Gabrielov, A., Gelfand, I., Gvishiani, A., and Rantsman, E., Formal morphostructural zoning of mountain territories, Geophysics, 1977, vol. 42(2), pp. 227–233.

    Google Scholar 

  3. Bongard, M.M., Vaintsvaig, M.N., Guberman, Sh.A., Izvekova, M.L., and Smirnov, M.S., Using a learning software for detection of oil-bearing layers, Geol. Geofiz., 1966, no. 6(II), pp. 15–29.

  4. Dzeboev B.A., Gvishiani A.D., Belov I.O., Agayan S.M., Tatarinov V.N., Barykina Yu.V. Strong Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai-Sayan-Baikal Region, М ≥ 6.0 // Izvestiya, Physics of the Solid Earth. 2019. Vol. 55. Is. 4. P. 563–575. https://doi.org/10.1134/S1069351319040050

  5. Dzeboev, B.A., Soloviev, A.A., Dzeranov, B.V., Karapetyan, J.K., and Sergeeva, N.A., Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, M ≥ 6.0. Variable EPA method, Russian Journal of Earth Sciences (RJES), 2019b, vol. 19, ES6005. https://doi.org/10.2205/2019ES000691

    Article  Google Scholar 

  6. Fikhtengolts, G.M., Osnovy matematicheskogo analiza (Principles of Mathematical Analysis), St. Petersburg: Lan, 2015, Parts 1 and 2.

  7. Gabrielov, A.M., Gorshkov, V.I., and Rantsman, E.Ya., An attempt at morphostructural regionalization using formal patterns, in Raspoznavanie i spektralnyi analiz v seismologii (Recognition and Spectral Analysis in Seismology), Vychisl. Seismol. 10, Keilis-Borok, V.I., Ed., 1977, pp. 50–58.

  8. Gelfand, I.M., Guberman, Sh.A., Izvekova, M.L., Keilis-Borok, V.I., and Rantsman, E.Ya., On criteria of high seismicity, Dokl. Akad. Nauk SSSR, 1972, vol. 202, no. 6, pp. 1317−1320.

    Google Scholar 

  9. Gelfand, I.M., Guberman, Sh.A., Keilis-Borok, V.I., Knopoff, L., Press, F., Rantsman, E.Ya., Rotvain, I.M., and Sadovsky, A.M., Conditions for the generation of large earthquakes: California and some other regions, in Issledovanie seismichnosti i modelei Zemli (Studies in Seismicity and Earth Models), Vychisl. Seismol. 9, Keilis-Borok, V.I., Ed., Moscow, 1976, pp. 3–91.

  10. Gorshkov, A. and Novikova, O., Estimating the validity of the recognition results of earthquake-prone areas using the ArcMap, Acta Geophysica, 2018, vol. 66, Iss. 5, pp. 843–853. https://doi.org/10.1007/s11600-018-0177-3

    Article  Google Scholar 

  11. https://ru.wikipedia.org [date of request: October 26, 2020].

  12. Gorshkov A.I., Soloviev A.A., Zharkikh J.I. Recognition of strong earthquake prone areas in the Altai–Sayan–Baikal Region // Doklady Earth Sciences. 2018. Vol. 479. Is. 1. P. 412–414. https://doi.org/10.1134/S1028334X1803025X

  13. Gvishiani, A.D. and Gurvich, V.A., Dinamicheskie zadachi klassifikatsii i vypukloe programmirovanie v prilozheniyakh (Dynamic Classification Problems and Convex Programming in Applications), Moscow: Nauka, 1992.

  14. Gvishiani A.D., Agayan S.M., Dzeboev B.A., Belov I.O. Recognition of Strong Earthquake–Prone Areas with a Single Learning Class // Doklady Earth Sciences. 2017. Vol. 474. Part 1. P. 546–551. https://doi.org/10.1134/S1028334X17050038

  15. Gvishiani A.D., Kaftan V.I., Krasnoperov R.I., Tatarinov V.N., Vavilin E.V. Geoinformatics and Systems Analysis in Geophysics and Geodynamics // Izvestiya, Physics of the Solid Earth. 2019. Vol. 55. Is. 1. P. 33–49. https://doi.org/10.1134/S1069351319010038

  16. Gvishiani A.D., Soloviev A.A., Dzeboev B.A. Problem of Recognition of Strong-Earthquake-Prone Areas: a State-of-the-Art Review // Izvestiya, Physics of the Solid Earth. 2020. Vol. 56. Is. 1. P. 1–23. https://doi.org/10.1134/S1069351320010048

  17. Kosobokov, V.G., Recognition of locations of future large earthquakes in Middle Asia and Anatolia using the Hamming method, Modeli stroeniya Zemli i prognoza zemletryasenii (Models of Earth Structure and Earthquake Prediction), Vychisl. Seismol. 14, Keilis-Borok, V.I., Moscow, 1981, pp. 76–81.

  18. Kosobokov, V.G. and Soloviev, A.A., Pattern recognition in earthquake hazard assessment problems, Chebychev. Sb., 2018, vol. 19, no. 4, pp. 53–88. https://doi.org/10.22405/2226-8383-2018-19-4-55-90

    Article  Google Scholar 

  19. Rantsman, E.Ya., Mesta zemletryasenii i morphostruktura gornykh stran (Earthquake Locations and the Morphostructure of Mountain Countries), Moscow: Nauka, 1979.

  20. Rantsman, E.Ya. and Glasko, M.P., Morfostrukturnye uzly—mesta ekstremalnykh prirodnykh yavlenii (Morphostructural Intersections as Locations of Extreme Natural Phenomena), Moscow: Media-Press, 2004.

  21. Soloviev A.A., Novikova O.V., Gorshkov A.I., Piotrovskaya E.P. Recognition of potential sources of strong earthquakes in the Caucasus region using GIS technologies // Doklady Earth Sciences. 2013. Vol. 450. Is. 2. P. 658–660. https://doi.org/10.1134/S1028334X13060159

  22. Soloviev A.A., Gvishiani A.D., Gorshkov A.I., Dobrovolsky M.N., Novikova O.V. Recognition of earthquake-prone areas: Methodology and analysis of the results // Izvestya, Physics of the Solid Earth. 2014. Vol. 50. Is. 2. P. 151–168. https://doi.org/10.1134/S1069351314020116

  23. Soloviev Al.An., Gorshkov A.I., Soloviev An.Al. Application of the data on the lithospheric magnetic anomalies in the problem of recognizing the earthquake prone areas // Izvestiya, Physics of the Solid Earth. 2016. Vol. 52. Is. 6. P. 803–809. https://doi.org/10.1134/S1069351316050141

  24. Vaintsvaig, M.N., An algorithm for the Kora pattern recognition learning, in Algoritmy obucheniya raspoznavaniyu obrazov (Algorithms for Training of Pattern Recognition), Moscow: Radio, 1973, pp. 8–12.

  25. Zadeh, L.A., Fuzzy sets, Information and Control, 1965, vol. 8, pp. 338–353.

    Article  Google Scholar 

Download references

Funding

The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of the scientific project No. 20-35-70054 “A systematic approach to the integration of recognition algorithms for assessing seismic hazard”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. A. Dzeboev.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gvishiani, A.D., Dzeboev, B.A., Agayan, S.M. et al. Fuzzy Sets of High Seismicity Intersections of Morphostructural Lineaments in the Caucasus and in the Altai–Sayan–Baikal Region. J. Volcanolog. Seismol. 15, 73–79 (2021). https://doi.org/10.1134/S0742046321020032

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0742046321020032

Keywords:

Navigation