Recognition of strong earthquake–prone areas with a single learning class

• Published in: Doklady earth sciences Vol. 474; no. 1; pp. 546 - 551
• Main Authors: Gvishiani, A. D., Agayan, S. M., Dzeboev, B. A., Belov, I. O.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01-05-2017; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Aversion learning; Coherence; Earth and Environmental Science; Earth Sciences; Earthquakes; Geochemistry; Geology; Geophysics; Machine learning; Mathematical models; Object recognition; Parameters; Seismic activity; Seismic design; Seismology; Serial learning
• ISSN: 1028-334X; 1531-8354
• DOI: 10.1134/S1028334X17050038

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 just on one “pure” high-seismic class. The new algorithm operates in the space of absolute values of the geological–geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with
М
≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.