2001 Fiscal Year Final Research Report Summary
Studies on prediction of strong ground motions for seenario earthquake using strong motion data
| Project/Area Number |
12680462
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Natural disaster science
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
IWATA Tomotaka DPRI, Kyoto Univ., Res. Associate, 防災研究所, 助手 (80211762)
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| Project Period (FY) |
2000 – 2001
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| Keywords | K-NET / KiK-net / CEORKA / Scenario earthquake / prediction of strong motion / Empirical Green's function method / Stress drop / Rupture propagation |
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| Research Abstract |
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.
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Research Products
(13 results)
