Design Ground Motion for Lifeline System Taking into Account Fault Rupture Process

应用倒谱分析将地震波解卷积为系统响应函数和一系列脉冲。基于误差最小化准则，提出了一种利用识别出的脉冲串识别震源机制的方法，并对模拟波形进行了分析，验证了反褶积方法的有效性。利用埃尔森特罗台阵14个台站记录的加速度图，对1979年加州Imperial Valley地震的断层机制进行了研究。由于解卷积后的14个脉冲序列包含了有关断裂机理的信息，因此，通过选择适当的断裂过程中的故障参数值，使解卷积后的脉冲序列与理论计算值之间的误差最小，可以估计由断裂速度、断裂起始点、断裂传播方向和上升时间等故障参数所定义的断裂过程。在断层面上识别出5个主要的小事件，证实了该断裂的多次破裂过程，识别出的地震矩在断层面上的分布与用不同方法研究的结果比较吻合。解析算法导致非线性系统反演和非唯一性的识别参数。为了克服这些问题，我们应用贝叶斯规则，它提供了一个定量的误差测量，考虑到先验信息的影响，用于识别参数。

The cepstrum analysis was applied to deconvolute a seismic wave into a system response function and a train of impulses. Based on several criteria to minimize error, we propose a method to identify the source mechanism using identified trains of impulses.Simulated waves were analyzed in order to examine the validity of the proposed deconvolution method. It was found that arrival time of an impulse was estimated within the error of two sampling intervals and intensity of impulse was proportional to the difference between the exact and estimated arrival time of the impulse.The acceleograms recorded at fourteen stations in El Centro Array are used to identify the fault mechanism of the 1979 Imperial Valley, California earthquake. Because deconvoluted fourteen trains of impulses contain information about the fault mechanism, the rupture process defined by such fault parameters as the rupture velocity, the starting point of rupture, the direction of the rupture propagation and the rise time can be estimated by minimizing the error between deconvoluted trains of impulses and theoretically calculated ones by selecting proper values of fault parameters in the rupture process. The multiple rupture process is confirmed by the fact that five major small events are identified on the fault surface.The identified distribution of seismic moment on the fault surface compares well with that investigated from different approach. The analytical algorithm led to the non-linear system inversion and non-uniqueness of identified parameters. To overcome these problems, we applied Bayes' rule which provided a quantitative error measure taking into account the effect of prior information for identifying parameters.