Recognition of Strong Earthquake–Prone Areas with a Single Learning Class. Caucasus, М ≥ 6.0

Author:

Dzeboev Boris,Dzeranov Boris,Pasishnichenko Maxim

Publisher

Springer International Publishing

Reference26 articles.

1. Gvishiani AD, Kossobokov VG (1981) On foundations of the pattern recognition results applied to earthquake-prone areas. Izv Acad Sci SSSR Fizika Zemli 2:21–36

2. Gvishiani A, Gorshkov A, Rantsman E, Cisternas A, Soloviev A (1988) Prognozirovanie mest zemletryasenii v regionakh umerennoi seismichnosti (Recognition of earthquake-prone areas in the regions of moderate seismicity). Nauka, Moscow

3. Soloviev AA, Gvishiani AD, Gorshkov AI, Dobrovolsky MN, Novikova OV (2014) Recognition of earthquake-prone areas: methodology and analysis of the results. Izv Phys Solid Earth 50(2):151–168. https://doi.org/10.1134/S1069351314020116

4. Gelfand IM, Guberman SA, Izvekova ML, Keilis-Borok VI, Rantsman EY (1973) Recognition of the locations of the probable occurrence of strong earthquakes. I. Pamir and Tien-Shan. In: Keilis-Borok VI (ed) Vychislitel’naya seismologiya. Vyp. 6. Vychislitel’nye i statisticheskie metody interpretatsii seismicheskikh dannykh (Computational seismology, vol. 6: computational and statistical methods for interpretation of seismic data). Nauka, Moscow, pp 107–133

5. Gelfand IM, Guberman SI, Izvekova ML, Keilis-Borok VI, Ranzman EJ (1972) Criteria of high seismicity, determined by pattern recognition. Tectonophysics 13:415–422

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