RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 19, ES6005, doi:10.2205/2019ES000691, 2019

Figure 3. Presentation of integral recognition results of earthquake-prone areas recognition in the Caucasus with $M \geq 6.0$ by the "Barrier-3" and "Cora-3" algorithms as a fuzzy set of neighborhoods of lineament intersections. Red color shows neighborhoods of intersections with the membership function $ \mu_{B_B, B_C} =1$, yellow – $ \mu_{B_B, B_C} = 0.5$, green – $ \mu_{B_B, B_C} = 0$.

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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.

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