Abstract Drawing on the known seismogenic structures of future large earthquakes, we propose to use empirical formulas to determine the parameters of length, width, seismic moment and sliding distribution of the large earthquake faults, and use the random finite fault method to predict future large earthquakes. Considering the MW7.8 earthquake that occurred in New Zealand on November 13, 2016, the ground motion time history and response spectrum of 12 bedrock stations, are simulated by stochastic finite fault method. The simulation error is determined by the average ratio of the simulated spectrum amplitude to the recorded spectrum amplitude. The results show that the period is in the range of 0-10 s and the simulation error is between 0.92-1.08. The standard deviation of the simulated error of different frequencies is not more than 1, and the width of 95% confidence interval has not changed significantly with the frequency. The simulation results reflect the average effect of ground motion records. Although a specific simulation may differ greatly from records, for engineering purposes we are interested in whether the simulation results are equivalent to the average results of seismic records. The quasi-random method is used to retrieve the initial rupture point and dislocation slip distribution of the New Zealand earthquake fault, simulate the ground motion, calculate the variation of simulation error with frequency, and obtain conclusions similar to those of the original model. This further confirms our proposed method of obtaining the seismic source parameters and the use of stochastic finite fault methods for the prediction of future large earthquakes is reliable, especially for the far-field simulation of large earthquakes.
LI Qicheng,HE Shugeng,MIN Ye et al. Simulation of Amberley New Zealand November 13, 2016 MW7.8 Earthquake Using Stochastic Finite Fault Method[J]. jgg, 2019, 39(12): 1237-1242.
LI Qicheng,HE Shugeng,MIN Ye et al. Simulation of Amberley New Zealand November 13, 2016 MW7.8 Earthquake Using Stochastic Finite Fault Method[J]. jgg, 2019, 39(12): 1237-1242.
