Ground-motion simulations within ShakeMap methodology: application to the 2008 Iwate-Miyagi Nairiku (Japan) and 1980 Irpinia (Italy) earthquakes

Abstract

ShakeMap package uses empirical Ground Motion Prediction Equations (GMPEs) to estimate the ground motion where recorded data are not available. Recorded and estimated values are then interpolated in order to produce a shaking map associated with the seismic event of interest. The ShakeMap approach better works in regions with dense stations coverage, where the observed ground motions adequately constrain the interpolation. In poorly instrumented regions, the ground motion estimate mainly relies on the GMPE, that account only for average characteristics of source and wave propagation processes. In this study we investigated the improvement of ShakeMap in the near fault area when including synthetic estimates. We focus on the 2008, Mw 7.0, Iwate-Miyagi Nairiku (Japan) earthquake as a case study because recorded by a huge number of stations. As first we calculated the shakemaps to be used as reference maps and then removed several subsets of stations from the original data-set, replacing them with: (i) the estimations of the ground motion obtained by using a specific GMPE valid for that area, using simple source information such as the earthquake magnitude and fault geometry; (ii) the peak values from synthetic time-histories computed with a hybrid deterministic-stochastic method for extended fault, using the rupture fault model obtained from the kinematic source inversion of strong-motion records. We evaluate the deviations from the reference map and the sensitivity to the number of sites where recordings are not available. Our results show that shakemaps are more and more reliable as the coverage of stations is dense and uniformly distributed in the near-source area. Moreover, the synthetics account for propagation and source properties in a more correct way than GMPE, and largely improve the results. The hybrid maps reach good fitting levels especially when synthetics are used to integrate real data and for particular strong-motion parameters and stations’ distribution.