Earthquake Forecasting Through Semi-periodicity Analysis of Labeled Point Processes

Abstract:

Large earthquakes have semi-periodic behavior as result of critically self-organized processes of stress accumulation and release in some seismogenic region. Thus, large earthquakes in a region constitute semi-periodic sequences with recurrence times varying slightly from periodicity. Nava et al., 2013 and Quinteros et al., 2013 realized that not all earthquakes in a given region need belong to the same sequence, since there can be more than one process of stress accumulation and release in it; they also proposed a method to identify semi-periodic sequences through analytic Fourier analysis. This work presents improvements on the above-mentioned method: the influence of earthquake size on the spectral analysis, and its importance in semi-periodic events identification, which means that earthquake occurrence times are treated as a labeled point process; the estimation of appropriate upper limit uncertainties to use in forecasts; and the use of Bayesian analysis to evaluate the forecast performance. This improved method is applied to specific regions: the southwestern coast of Mexico, the northeastern Japan Arc, the San Andreas Fault zone at Parkfield, and northeastern Venezuela.