Parameter estimation for predicting near-fault strong ground motion and its application to Lushan earthquake in China

Stochastic finite-fault modelling based on the dynamic corner frequency is a widespread method for synthesizing near-fault ground motions at high frequencies and has been proven to be an effective tool for simulating high-frequency ground motions by researchers globally. Many input parameters, which have more uncertain effects on the synthetic result, are related to sources, sites, and paths in stochastic modelling. Thus, it is difficult to select reasonable parameters. When using the stochastic finite-fault method, the key link to be considered is how to determine the input parameters required for simulations. Based on this fact, this study analyzed and discussed the sensitivity of several important input parameters in the simulation parameters to the simulation results. The results showed that for the Class C site stations, the crustal and local site amplification factors must be considered simultaneously. Overall, the influence of the high-frequency attenuation parameter κ0 calculated using different methods on the simulation results is not evident. For near-fault stations, various upper-edge buried depth synthetic response spectra can be set, and finally, the average value is taken as the final synthetic recorded response spectra. When using the random source model, the rupture starting point must be set on the subfault in the middle along the fault length and width direction. The conclusions provide a basis for a highly reasonable and rapid determination of input parameters when using the stochastic finite-fault simulations for practical engineering applications and can improve the simulation accuracy to apply the finite source modelling to rebuild scenarios and evaluate and alleviate the seismic hazard in the target region. •Effects of kappa models and fault depth on the simulation results are discussed.•Effects of initiation points and site effect on the synthesis results are analyzed.•Recommended algorithms of the key input parameters are given.