加速度
衰减
地震学
强地震动
光谱加速度
断层(地质)
地质学
计算机科学
声学
峰值地面加速度
地震动
物理
光学
经典力学
作者
Tianjia Wang,Xu Xie,Longfei Ji
摘要
ABSTRACT The stochastic finite-fault method (EXSIM) has been extensively used for simulating ground motion at high frequencies. However, its poor performance in low-frequency simulations is a limiting factor that restricts its engineering application. Refining the representation of the radiation pattern in the finite-fault method is an effective strategy to improve low-frequency simulations; to this end, a frequency-dependent radiation pattern has been considered by several researchers. However, this strategy fails to provide an accurate simulation of seismic-wave propagation at distances beyond the near-fault region. Researchers have proposed various approaches for characterizing the radiation pattern variation with distance. This study introduces frequency- and distance-dependent radiation patterns of S waves to the EXSIM. The near-field acceleration records in the east–west and north–south directions of the 2013 Ms 7.0 Lushan earthquake were reconstructed. The proposed method was verified by: (1) comparing broadband simulation results obtained by the improved method with observed results, (2) conducting a misfit analysis to compare the model bias between the improved and original methods, and (3) comparing the observed and simulated peak ground acceleration data with the predicted values of the ground-motion prediction equations (GMPEs) to verify the effectiveness of the GMPEs in describing the regional ground-motion attenuation. The results indicated that the 5%-damped pseudo spectral accelerations at high frequencies (1–20 Hz) and acceleration time history simulated by the improved method were consistent with the observed values. Furthermore, the improved method effectively optimizes the simulation effect at low frequencies (0.05–1 Hz) compared with the original method. Thus, the improvement in the representation of the radiation pattern in EXSIM can better estimate broadband ground motion in the study area.
科研通智能强力驱动
Strongly Powered by AbleSci AI