Estimating the validity of the recognition results of earthquake-prone areas using the ArcMap
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DOI: 10.1007/s11600-018-0177-3
Abstract: In 1972, V. Keilis-Borok and I. Gelfand introduced the phenomenological approach based on the morphostructural zoning and pattern recognition for identification of earthquake-prone areas. This methodology identifies seismogenic nodes capable of generating strong earthquakes on… read more here.
Keywords: methodology; earthquake prone; recognition; seismogenic nodes ... See more keywords
Recognition of strong earthquake–prone areas with a single learning class
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DOI: 10.1134/s1028334x17050038
Abstract: This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning… read more here.
Keywords: recognition; earthquake prone; class; prone areas ... See more keywords
Significant Earthquake-Prone Areas in the Altai–Sayan Region
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DOI: 10.1134/s1069351318030035
Abstract: This paper continues the series of our works on recognizing the areas prone to the strongest, strong, and significant earthquakes in different mountain countries with the use of the algorithmic Formalized Clustering And Zoning (FCAZ)… read more here.
Keywords: earthquake prone; altai sayan; significant earthquake; sayan region ... See more keywords
Strong-Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai–Sayan–Baikal Region, М ≥ 6.0
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DOI: 10.1134/s1069351319040050
Abstract: A new version of the Barrier algorithm is proposed for recognition of strong-earthquake prone regions based on training over a single reliable training class. The modification of the algorithm consists in creating blocks that reveal… read more here.
Keywords: earthquake prone; region; altai sayan; recognition ... See more keywords