Abstract
We image the rupture process of the 2000 Western Tottori earthquake (Mw = 6.6) through fitting of strong motion and GPS data. We consider an observational network consisting of 18 strong motion and 16 GPS stations, located within three fault lengths from the epicentre. We assume a planar fault and compute Green's functions for a 1-D velocity model. The earthquake rupture is described as a shear dislocation parameterized in terms of peak slip velocity, rake angle, rupture time and rise time, defined on a regular grid of nodes on the fault surface and derived at inner points through bilinear interpolation. Our inversion procedure is based on a Bayesian approach. The solution of the inverse problem is stated in terms of a posterior probability density function (pdf), representing the conjunction of prior information with information contained in the data and in the physical law relating model parameters with data. Inferences on model parameters are thus expressed in terms of posterior marginal pdfs. Due to the non-linearity of the problem, we use a Markov Chain Monte Carlo (MCMC) method, based on the Metropolis algorithm, to compute posterior marginals. Except for a few cases posterior marginals do not show a Gaussian-like distribution. This prevents us from providing a mean model and from characterizing uncertainties in terms of standard deviations only. Resolution on each single parameter is analysed by looking at the difference between prior and posterior marginal pdfs. Posterior marginals indicate that the best resolved feature is a major slip patch (peak value of 311 ± 140 cm), located between the hypocentre and the top edge of the fault, centered at a depth of 4.5 km. This shallow slip patch is triggered about 3 s after the earthquake nucleated and required about 4 s to reach its final slip value. The presence of this shallow slip patch is common to all previous studies. In contrast to some previous studies, we do not identify any significant slip (>1 m) at the bottom of the fault. We also compare inferences from both strong motion and GPS data with inferences derived from strong motion data only. In both cases the shallow slip patch is identified. At other locations, the main effect of the GPS data is in reducing the probability associated with high values of slip. GPS data reduce the presence of spurious fault slip and therefore strongly influence the resulting final seismic moment.