10:45 AM - 11:00 AM
[SSS04-07] The empirical characteristics evaluated by Generalized Inversion Technique and the estimation method of equivalent site amplifications at arbitrary sites base on those characteristics
★Invited Papers
Keywords:Generalized Inversion Technique, Site amplification, Spatial Interpolation
I and my collaborators have been studying about the evaluation of the three basic characteristics, i.e. source, pass, and site factors, from strong motion observation records by the generalized inversion technique, is called GIT in general, in Japan (e.g. Nakano et al., 2015; Nakano and Kawase, 2019; Nakano and Kawase, 2021).
Considering to apply the empirical characteristics to the broad band strong motion prediction for engineering purpose at an arbitrary site, the site amplification is one of the most important factors. Based on our previous studies, the site amplification with S-wave portion (SA-S) estimated by GIT is good agreement with 1D theory at some sites, and, the site amplification with S- and Surface-wave (SA-SS) including long-period component is evaluated appropriately. SA-S is also called as Horizontal Site Amplification Factors (HSAFs). Then you can use it to the strong motion prediction directly for precise predictions at that site. But, in many cases, the strong motion observation has not been performed at where you want to design and construct a new building or something.
Nakano and Kawase (2021) recommends the ways to estimate the SA-S without GIT: 1) The method using MHVSRs at a target site proposed by Kawase et al. (2018), 2) The theoretical transfer function based on 1D-theory using a subsurface structure provided by HERP (J-SHIS or JIVSM) with PS-logging data at a target site. You could estimate SA-S at an arbitrary site using the abovementioned methods. However, SA-S is reflected only S-wave portion. For considering Coda-wave effects including long-period component lager than roughly 1s to the site amplification at an arbitrary site, WSR method, which is correction function converting SA-S to SA-SS based on two step interpolation technique, has been proposed by previous studies (e.g. Nakano, 2020; Nakano and Kawase, 2021; Nakano, 2021; Kawase, 2021).
WSR is defined as ratio of the SA-SS and SA-S. For the prediction of WSR at an arbitrary site, I adapted the two steps interpolation technique. First, I estimated the WSR on the unified grid space from the strong motion observation points by the minimum curvature interpolation technique proposed by Smith and Wessel (1990). Next, I evaluated the WSR at the target site by the interpolation technique using the data distributed on the unified grid space based on the Shepard's method (e.g. Renka, 1999). In the previous papers, we have reported the verification of the performance of the two steps interpolation technique mentioned above (e.g. Nakano, 2020; Nakano and Kawase, 2021).
Figure 1 shows that the WSR at the four sites (OSK005, OSKH02, AIC003, TKY007) and the maps of the WSR in Japan at three frequencies. As you can see, in the figure 1 (a) – (d), all lines are over 1 at each site. In the long period larger than 1 second, WSR is relatively high. We think it is because Coda-waves affect especially to the long-period component of site amplifications. As the figure 1 (e) – (g), I found that the spatial changes of WSR were not large relative to the SA-S or SA-SS.
In this presentation, I will introduce the briefly explanation of the empirical characteristics evaluated by GIT focusing especially on the site amplifications, the estimation method, i.e. WSR method, of site amplification at arbitrary sites in details, and the case of the strong motion prediction applied WSR technique.
Considering to apply the empirical characteristics to the broad band strong motion prediction for engineering purpose at an arbitrary site, the site amplification is one of the most important factors. Based on our previous studies, the site amplification with S-wave portion (SA-S) estimated by GIT is good agreement with 1D theory at some sites, and, the site amplification with S- and Surface-wave (SA-SS) including long-period component is evaluated appropriately. SA-S is also called as Horizontal Site Amplification Factors (HSAFs). Then you can use it to the strong motion prediction directly for precise predictions at that site. But, in many cases, the strong motion observation has not been performed at where you want to design and construct a new building or something.
Nakano and Kawase (2021) recommends the ways to estimate the SA-S without GIT: 1) The method using MHVSRs at a target site proposed by Kawase et al. (2018), 2) The theoretical transfer function based on 1D-theory using a subsurface structure provided by HERP (J-SHIS or JIVSM) with PS-logging data at a target site. You could estimate SA-S at an arbitrary site using the abovementioned methods. However, SA-S is reflected only S-wave portion. For considering Coda-wave effects including long-period component lager than roughly 1s to the site amplification at an arbitrary site, WSR method, which is correction function converting SA-S to SA-SS based on two step interpolation technique, has been proposed by previous studies (e.g. Nakano, 2020; Nakano and Kawase, 2021; Nakano, 2021; Kawase, 2021).
WSR is defined as ratio of the SA-SS and SA-S. For the prediction of WSR at an arbitrary site, I adapted the two steps interpolation technique. First, I estimated the WSR on the unified grid space from the strong motion observation points by the minimum curvature interpolation technique proposed by Smith and Wessel (1990). Next, I evaluated the WSR at the target site by the interpolation technique using the data distributed on the unified grid space based on the Shepard's method (e.g. Renka, 1999). In the previous papers, we have reported the verification of the performance of the two steps interpolation technique mentioned above (e.g. Nakano, 2020; Nakano and Kawase, 2021).
Figure 1 shows that the WSR at the four sites (OSK005, OSKH02, AIC003, TKY007) and the maps of the WSR in Japan at three frequencies. As you can see, in the figure 1 (a) – (d), all lines are over 1 at each site. In the long period larger than 1 second, WSR is relatively high. We think it is because Coda-waves affect especially to the long-period component of site amplifications. As the figure 1 (e) – (g), I found that the spatial changes of WSR were not large relative to the SA-S or SA-SS.
In this presentation, I will introduce the briefly explanation of the empirical characteristics evaluated by GIT focusing especially on the site amplifications, the estimation method, i.e. WSR method, of site amplification at arbitrary sites in details, and the case of the strong motion prediction applied WSR technique.