Studies on prediction of strong ground motions for seenario earthquake using strong motion data

本研究的目的是将Irikura et al.（1999）提出的强地震动预测方法应用于内陆地壳情景地震。我们的研究结果如下：1。强运动数据库建设：我们收集了Kinki地区的强运动网络数据，如K-NET、KiK-net、JMA、CEORKA等强运动数据。构造震源模型的信息收集：将活动断层信息与地震信息一起收集，用于构造地壳地震的地震情景。考虑Hanaore断层、Biwako-Seigan断层和Yamasaki断层。经验格林函数与随机格林函数的比较：在一些地点将小事件记录与随机格林函数波形进行比较，需要经验包络估计来解释随机格林函数的地面运动。基于情景地震的强震估计：对于一些活动断层，我们构建了震源情景，并利用经验格林函数估计地面运动。断层的几何形状、破裂方式和应力降参数都对地震动分布有很大影响。

In this research, our aim is to apply method of strong ground motion prediction proposed be Irikura et al.(1999) to inland crustal scenario earthquakes. Our research results are as follows,1. Strong motion database construction : We collected strong motion network data, such as, K-NET, KiK-net, JMA, CEORKA, and other strong motion data in Kinki area.2. Collection of information for constructing source model : Active fault information together with seismic information are collected for constructing earthquake scenarios for crustal earthquakes. Hanaore, Biwako-Seigan, and Yamasaki faults are considered.3. Comparisons of empirical and stochastic Green functions : Small event records are compared with stochastic Green function waveforms at some sites and empirical envelope estimation are needed to explain ground motions by the stochastic Green function.4. Strong motion estimation by scenario earthquakes : For some active faults, we constructed the source scenarios and estimate ground motions by empirical Green's function. Not only fault geometry, fault rupture pattern, but stress drop parameters much affect ground motion distribution.