1994 Fiscal Year Final Research Report Summary
Extraction of ground structure for seismic zoning using inverse analysis
| Project/Area Number |
05680365
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Natural disaster science
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
TOKI Kenzo Kyoto Univ., Engineering, Professor, 工学部, 教授 (10027229)
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| Co-Investigator(Kenkyū-buntansha) |
SAWADA Sumio Kyoto Univ., Engineering, Instructor, 工学部, 助手 (70187293)
KIYONO Junji Yamaguchi Univ, Engineering, Associate Professor, 工学部, 助教授 (00161597)
SATO Tadanobu Kyoto Univ., D.P.R.I,Professor, 防災研究所, 教授 (00027294)
IRIKURA Kojiro Kyoto Univ., D.P.R.I,Professor, 防災研究所, 教授 (10027253)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Seismic zoning / Inverse analysis / Soil structure / scattered wave / AL method / R / T matrix method / Layrd ground |
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| Research Abstract |
It is very impotant to reduce ground structure of wide area from several strong motion records. A modified Reflection/Transmission Matrices method and stcastic AL method are proposed to consider scattered wave induced on the boundary between soil layrs. The case studies using this method show that the scattered wave has two effects on the characteristics of the ground response. One is to give other peaks on frequency response function which can not be represented by one dimensional analysis and the other is the same effect with the frequency dependent damping.
Next we develop a method to determine the boundary shape of ground structures using seismograms observed on the surface through a waveform inversion scheme. This study examines numerically how to determine the boundary shape from observed records on the surface in the case of the two dimensional SH problem. For simplicity, we parameterize only the boundary shape. When an appropriate model is given, we can evaluate the boundary shape accurately even though the waveform data at only a few surface stations are given.
This study also explores the potential of using neural network approaches to identify the dynamic characteristics of ground structural system. Because of self-learning nature of neural network the identified dynamic characteristics are strongly affected by the level of noise cotained in the teaching signals. Using the Karman filtering technique, a method to identify the dynamic characteristics of structural system proof against contaminating noise in teaching signals has been developed.
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Research Products
(20 results)
