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Aftershocks rates seem to follow a power law decay, but the question of the aftershock frequency immediately after an earthquake remains open. We estimate an average aftershock decay rate within one day in southern California by stacking in time different sequences triggered by main shocks ranging in magnitude from 2.5 to 4.5. Then we estimate the time delay before the onset of the power law aftershock decay rate. For the last 20 years, we observe that this time delay suddenly increase after large earthquakes, and slowly decreases at a constant rate during periods of low seismicity. In a band-limited power law model such variations can be explained by different patterns of stress distribution at different stages of the seismic cycle. We conclude that, on regional length scales, the brittle upper crust exhibits a collective behavior reflecting to some extent the proximity of a threshold of fracturing
In order to examine variations in aftershock decay rate, we propose a Bayesian framework to estimate the {K, c, p}-values of the modified Omori law (MOL), lambda(t) = K(c + t)(-p). The Bayesian setting allows not only to produce a point estimator of these three parameters but also to assess their uncertainties and posterior dependencies with respect to the observed aftershock sequences. Using a new parametrization of the MOL, we identify the trade-off between the c and p-value estimates and discuss its dependence on the number of aftershocks. Then, we analyze the influence of the catalog completeness interval [t(start), t(stop)] on the various estimates. To test this Bayesian approach on natural aftershock sequences, we use two independent and non-overlapping aftershock catalogs of the same earthquakes in Japan. Taking into account the posterior uncertainties, we show that both the handpicked (short times) and the instrumental (long times) catalogs predict the same ranges of parameter values. We therefore conclude that the same MOL may be valid over short and long times.