Reproducing the Spatial Characteristics of High-Frequency Ground Motions for the 1850M 7.5 Xichang Earthquake

Abstract Reproducing the spatial characteristics of large historical earthquakes and predicting the strong ground motions of future destructive large earthquakes through actual small earthquakes have high-practical value. The empirical Green’s function method is a numerical simulation method that can impart real seismic information in synthetic ground motions. In this article, we use data from the 2018 M 5.1 Xichang earthquake to reproduce the ground-motion characteristics of the 1850 M 7.5 Xichang earthquake using the empirical Green’s function method. The uncertainties of the parameters, such as the number, area, and locations of asperities, are considered. The synthetic time histories, peak ground accelerations (PGAs), and response spectra are obtained through simulation. The main results are as follows. (1) The synthetic Xichang earthquake (such as the ground-motion intensity and attenuation characteristic of the PGA) matches well with the M 8.0 Wenchuan earthquake and M 7.3 Jiji earthquake. When the number of asperities is 1 or 2, the PGA characteristics of the Xichang earthquake match well not only with the Next Generation Attenuation-West2 (2014) ground-motion model in the range of 100 km but also with the seismic ground-motion parameter zonation map of China in the range of 20–100 km. (2) The prediction results based on the asperity source model are relatively reliable in the range of 20–100 km. The one-asperity and two-asperity models of the Xichang earthquake match better than the three-asperity and four-asperity models. (3) We can speculate that when the M 7.5 earthquake struck the Xichang area, the damage was relatively strong. The PGA may have exceeded 1.0g in the meizoseismal area, and the seismic intensity in the meizoseismal area may have reached or exceeded a degree of X–XI. Therefore, the synthesized M 7.5 Xichang earthquake has the strength characteristics of a large destructive earthquake.