It is well known that quantitative estimation of slip distributions on fault plane is one of the most important issues for earthquake source inversion related to the fault rupture process. The characteristics of slip distribution on the main fault play a fundamental role to control strong ground motion pattern. A large amount of works have also suggested that variable slip models inverted from longer period ground motion recordings are relevant for the prediction of higher frequency ground motions. Zhang et al.(Chin J Geophys 56:1412–1417,2013) and Wang et al.(Chin J Geophys 56:1408–1411, 2013)published their source inversions for the fault rupturing process soon after the April 20, 2013 Lushan earthquake in Sichuan,China. In this study, first, we synthesize two forward source slip models: the value of maximum slip, fault dimension, size, and dimension of major asperities, and corner wave number obtained from Wang’s model is adopted to constrain the generation of k-2model and crack model. Next, both inverted and synthetic slip models are employed to simulate the ground motions for the Lushan earthquake based on the stochastic finite-fault method. In addition, for a comparison purpose, a stochastic slip model and another k-2model(k-2model II) with2 times value of corner wave number of the original k-2model(k-2model I) are also employed for simulation for Lushan event. The simulated results characterized by Modified Mercalli Intensity(MMI) show that the source slip models based on the inverted and synthetic slip distributions could capture many basic features associated with the ground motion patterns.Moreover, the simulated MMI distributions reflect the rupture directivity effect and the influence of the shallow velocity structure well. On the other hand, the simulated MMI bystochastic slip model and k-2model II is apparently higher than observed intensity. By contrast, our simulation results show that the higher frequency ground motion is sensitive to the degree of slip roughness; therefore, we suggest that, for realistic groundmotion simulations due to future earthquake, it is imperative to properly estimate the slip roughness distribution. It is well known that quantitative estimation of slip distributions on fault plane is one of the most important issues for earthquake source inversion related to the fault rupture process. The characteristics of slip distribution on the main fault play a fundamental role to control strong ground motion pattern . A large amount of works have also suggested that variable slip models inverted from longer period ground motion recordings are relevant for the prediction of higher frequency ground motions. Zhang et al. (Chin J Geophys 56: 1412-1417, 2013) and Wang et al. (Chin J Geophys 56: 1408-1411, 2013) published their source inversions for the fault rupturing process soon after the April 20, 2013 Lushan earthquake in Sichuan, China. In this study, first, we synthesize two forward source slip models : the value of maximum slip, fault dimension, size, and dimension of major asperities, and corner wave number obtained from Wang’s model is adopted to constrain the generation of k-2 model and crack In addition, for a comparison purpose, a stochastic slip model and another k-2 model (k- 2model II) with2 times value of corner wave number of the original k-2model (k-2model I) are also employed for simulation for Lushan event. The simulated results characterized by Modified Mercalli Intensity (MMI) show that the source slip models based on the inverted and synthetic slip distributions could capture many basic features associated with the ground motion patterns. Moreover, the simulated MMI distributions reflect the rupture directivity effect and the influence of the shallow velocity structure well. On the other hand, the simulated MMI by stochastic slip model By contrast, our simulation results show that the higher frequency ground motion is sensitive to the degree ofslip roughness; therefore, we suggest that, for realistic groundmotion simulations due to future earthquake, it is imperative to properly estimate the slip roughness distribution.