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

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dc.contributor.author Dzeboev B.A.
dc.contributor.author Soloviev A.A.
dc.contributor.author Dzeranov B.V.
dc.contributor.author Karapetyan J.K.
dc.contributor.author Sergeeva N.A.
dc.date.accessioned 2020-08-21T06:28:50Z
dc.date.available 2020-08-21T06:28:50Z
dc.date.issued 2019
dc.identifier https://cyberleninka.ru/article/n/strong-earthquake-prone-areas-recognition-based-on-the-algorithm-with-a-single-pure-training-class-ii-caucasus-m-geq-6-0-variable-epa-method
dc.identifier Федеральное государственное бюджетное учреждение науки Геофизический центр Российской академии наук
dc.identifier.citation Russian Journal of Earth Sciences, 2019, 19, 6
dc.identifier.uri https://repository.geologyscience.ru/handle/123456789/17566
dc.description.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.
dc.publisher Федеральное государственное бюджетное учреждение науки Геофизический центр Российской академии наук
dc.subject EARTHQUAKE-PRONE AREAS RECOGNITION
dc.subject EPA
dc.subject CORA-3
dc.subject BARRIER-3
dc.subject CAUCASUS
dc.subject SEISMIC HAZARD ASSESSMENT
dc.subject FUZZY SET
dc.title STRONG EARTHQUAKE-PRONE AREAS RECOGNITION BASED ON THE ALGORITHM WITH A SINGLE PURE TRAINING CLASS. II. CAUCASUS, M $ \geq $ 6.0. VARIABLE EPA METHOD
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dc.type Article


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