一般社団法人資源・素材学会 2020年度春季大会

講演情報(2020年1月24日付 確定版)

一般講演

開発機械/岩盤工学/資源開発技術

2020年3月17日(火) 08:40 〜 11:50 第4会場 (6号館 2階 625講義室)

司会:羽柴 公博(東京大学)、武川 順一(京都大学)

09:20 〜 09:40

[3K62501-09-03] 全波形逆解析による地震の震源メカニズムの推定 (発表者:修士課程)

○JANG Iktae1、三ケ田 均1、武川 順一1 (1. 京都大学)

キーワード:全波形逆解析、震源メカニズム、数値実験

For realization of the safe society from an earthquake, an early warning system which is necessary to obtain the accurate data issued to the public has been widely introduced in earthquake prone countries such as Japan and America. One of the most important tasks for considering the accuracy and the promptness of information when an earthquake occurs is estimating the earthquake source mechanism. Many recent studies have proposed methods for estimating physical mechanism of earthquake source and the centroid-moment tensor (CMT) solution that represents the stress tensor centered at the hypocenter at the occurrence moment could be one alternative. However, it is required to consider the location of a fault plane and the time sequence of fault propagation which means we need to refer to spatial distribution of aftershocks for the determination of the fault plane among two nodal planes.

Since contribution of quick estimation of focal mechanism to disaster mitigation is obvious, developing the method for estimating focal mechanism immediately has become an important task. To solve this problem, Cordrie and Mikada (2017) proposed a novel way which uses full waveforms for the estimation. Full-waveform inversion (FWI) has recently been proposed to estimate high resolution seismic velocity model (Tarantola, 1986), however, it has not been investigated the applicability of estimating fault parameters using inversion method yet.

In view of this point, we consider developing the estimation method of earthquake source parameters using FWI and as a first step in this study, we investigate the applicability of FWI evaluating three fault parameters (strike, dip and rake) assuming a known source time function. The numerical results indicate that FWI can estimate the fault parameters without long time observation.

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