6:15 PM - 7:30 PM
[SSS32-P01] Inference of a slip distribution from aftershock data and friction law: a Bayesian model with a prior of magnitude
Keywords:slip distribution, aftershocks, Bayesian estimation, prior distribution, Markov chain Monte Carlo method
One of the problems in this method is how to determine the strength of the roughness penalty objectively. In many cases of seismological/geophysical studies, the strength is determined by the principle of the minimization of Akaike's Bayesian Information Criterion [ABIC; Akaike, 1980] and ABIC is computed through the Laplace approximation [Tierny and Kadane, 1986]. However, because of some technical reasons originated from the formula of the friction law, the Laplace approximation is not applicable to this method and the computation of the value of ABIC is impractical.
This study proposes that the information on the magnitude of a mainshock is incorporated in the Bayesian model. This is because it has been empirically found that the amplitudes of the estimated slip in the subfaults or the corresponding magnitude to the estimated slip distribution much depends on the strength of the roughness penalty; if we impose a constraint on the magnitude, then the appropriate strength could be chosen objectively. To implement this idea, a prior distribution of the magnitude of a mainshock is constructed. It is supposed to be a normal distribution of which mean is retrieved from the Global CMT catalogue and standard deviation is given from Kagan . Then, the posterior distributions of the strength and the spatial slip distribution are computed simultaneously through the Markov chain Mote Carlo method. This framework provides the practical computational method to estimate the spatial slip distribution of a mainshock inferred from its aftershock data.
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