STRONG EARTHQUAKE-PRONE AREAS RECOGNITION BASED ON THE ALGORITHM WITH A SINGLE PURE TRAINING CLASS. II. CAUCASUS, M $ \geq $ 6.0. VARIABLE EPA METHOD

Dzeboev B.A.; Soloviev A.A.; Dzeranov B.V.; Karapetyan J.K.; Sergeeva N.A.

STRONG EARTHQUAKE-PRONE AREAS RECOGNITION BASED ON THE ALGORITHM WITH A SINGLE PURE TRAINING CLASS. II. CAUCASUS, M $ \geq $ 6.0. VARIABLE EPA METHOD

Dzeboev B.A.; Soloviev A.A.; Dzeranov B.V.; Karapetyan J.K.; Sergeeva N.A.

xmlui.dri2xhtml.METS-1.0.item-citation: Russian Journal of Earth Sciences, 2019, 19, 6

Date: 2019

Abstract:

Strong earthquake-prone areas recognition (𝑀≥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.

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