Radial Basis Function Neural Networks to Foresee Aftershocks in Seismic Sequences Related to Large Earthquakes
Radial Basis Function Neural Network are known in scientific literature for their abilities in function approximation. Above all, this particular kind of Artificial Neural Network is applied to time series forecasting in non-linear problems, where estimation of future samples starting from already detected quantities is very hardly. In this paper Radial Basis Function Neural Network was implemented in order to predict the trend of n(t) for aftershocks temporal series, that is the numerical series of daily-earthquake’s number occurred after a great earthquake with magnitude M > 7.0 Richter. In particular we implemented the RBF-NN for the Colfiorito seismic sequence. The seismic sequences considered in this work are obtained following criteria already known in scientific literature , . Results of proposed approach are very encouraging.
KeywordsRadial Basis Function Input Vector Radial Basis Function Neural Network Great Earthquake Aftershock Sequence
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- 1.Caccamo, D., Barbieri, F.M., D’Amico, S., Lagan, C., Parrillo, F.: The temporal series of the New Guinea aftershock sequence. P.E.P.I. 153(4), 175–180 (2005)Google Scholar
- 2.Caccamo, D., Barbieri, F.M., Lagan, C., D’Amico, S., Parrillo, F.: A study about the aftershock sequence of 27 December 2003 in Loyalty Islands. B.G.T.A. (in press, 2006)Google Scholar
- 4.Utsu, T., Seki, A.: A relation between the area of aftershock region and the energy of mainshock. J. Seismol. Soc. Jpn 7, 233–240 (1954)Google Scholar
- 5.Utsu, T.: Aftershocks and earthquake statistics (I) source parameters which characterize an aftershock sequence and their interrelations. J. Fac. Sci. Hokkaido Univ., Ser. VII 3, 129–195 (1969)Google Scholar
- 6.Gutenberg, B., Richter, C.F.: Earthquake magnitude, intensity, energy and acceleration. Bull. Seism. Soc. Am. 32, 162–191 (1942)Google Scholar
- 7.D’Amico, S., Caccamo, D., Barbieri, F.M., Lagan, C.: Some methodological aspect related to the prediction of large aftershocks. E.S.C. 2004, XXIX General Assembly, book of abstracts, pp. 132–133(2004)Google Scholar
- 9.Christodoulou, C., Goergiopoulos, M.: Application in Neural Networks in Electromagnetics. Artech House Publishers, Norwood Massachussetts (2001)Google Scholar
- 10.Parrillo, F., D’Amico, S., Cacciola, M., Morabito, F.C., Barrile, V., Versaci, M., Caccamo, D.: Reti Neurali Radial Basis Function e metodo Delta/Sigma per la previsione di forti repliche. Atti del XXIV Convegno Gruppo Nazionale Geofisica della Terra Solida, pp. 197–200 (2005)Google Scholar