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Abstract  We constructed a prototype of the basin and crustal structure model for the Kinki area, southwest of Japan, for the simulation of strong ground motions of hypothetical crustal and subduction earthquakes. We collected results of the deep seismic velocity profiles obtained by the reflection experiments and seismic imaging results, which were conducted in the Kinki area. The obtained profiles give underground velocity structures of the crust, from the surface to the subducting slab. We also gather the basin velocity structure information of the Osaka, Kyoto, Nara, and Ohmi basins. To examine the applicability of the constructed velocity structure model to the ground motion simulation, we simulated waveforms of an intermediate size event occurred near the source area of the hypothetical subduction earthquakes. Simulated ground motions using the basin and crustal velocity structure model are fairly well reproducing the observations at most of stations, and the constructed basin and crustal velocity structure model is applicable for the long-period ground motion simulations. [ABSTRACT FROM AUTHOR]

In the estimation of spatiotemporal slip models, kinematic source inversions using Akaike's Bayesian Information Criterion (ABIC) and the multiple-time-windowmethod have often been used. However, there are cases in which conventional ABIC-based source inversions do not work well in the determination of hyperparameters when a non-negative slip constraint is used. In order to overcome this problem, a new source inversion method was developed in this study. The new method introduces a fully Bayesian method into the kinematic multiple-time-window source inversion. The multiple-time-window method is one common way of parametrizing a source time function and is highly flexible in terms of the shape of the source time function. The probability distributions of model parameters and hyperparameters can be directly obtained by using the Markov chain Monte Carlo method. These probability distributions are useful for simply evaluating the uniqueness and reliability of the derived model, which is another advantage of a fully Bayesian method. This newly developed source inversion method was applied to the 2011 Ibaraki-oki, Japan, earthquake (Mw 7.9) to demonstrate its usefulness. It was demonstrated that the problem with using the conventional ABIC-based source inversion method for hyperparameter determination appeared in the spatiotemporal source inversion of this event and that the newly developed source inversion could overcome this problem. [ABSTRACT FROM AUTHOR]