RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 19, ES6005, doi:10.2205/2019ES000691, 2019
B. A. Dzeboev, A. A. Soloviev, B. V. Dzeranov, J. K. Karapetyan, N. A. Sergeeva
Strong earthquake-prone areas recognition ($M \geq 6.0$) in the Caucasus is performed by means of the new "Barrier-3" pattern recognition algorithm. The obtained result is compared with potentially high seismicity zones recognized previously using the "Cora-3" pattern recognition algorithm. It is proposed to define an interpretation of the integral recognition result by the "Barrier-3" and "Cora-3" algorithms as a fuzzy set of recognition objects in the vicinity of which strong earthquakes may occur in the Caucasus.
Received 1 November 2019; accepted 6 November 2019; published 5 December 2019.
Citation: Dzeboev B. A., A. A. Soloviev, B. V. Dzeranov, J. K. Karapetyan, N. A. Sergeeva (2019), Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, $M \geq $ 6.0. Variable EPA method, Russ. J. Earth Sci., 19, ES6005, doi:10.2205/2019ES000691.
Copyright 2019 by the Geophysical Center RAS.