The main purpose of the site classification or velocity determination at a target site is to obtain or estimate the horizontal site amplification factor (HSAF) at that site during future earthquakes because HSAF would have significant effects on the strong-motion characteristics. We have been investigating various kinds of methods to delineate the S-wave velocity structures and the subsequent HSAF, as precisely as possible. After the advent of the diffuse field concept, we have derived a simple formula based on the equipartitioned energy density observed in the layered half-space for incident body waves. In this study, based on the diffuse field concept, together with the generalized spectral inversion technique (GIT), we propose a method to directly estimate the HSAF of the S-wave portion from the horizontal-to-vertical spectral ratio of earthquakes (eHVSRs). Because the vertical amplification is included in the denominator of eHVSR, it cannot be viewed as HSAF without correction. We used GIT to determine both the HSAF and the vertical site amplification factor (VSAF) simultaneously from strong-motion data observed by the networks in Japan and then deduced the log-averaged vertical amplification correction function (VACF) from VSAFs at a total of 1678 sites in which 10 or more earthquakes have been observed. The VACF without a category has a constant amplitude of about 2 in the frequency range from 1 to 15 Hz. By multiplying eHVSR by VACF, we obtained the simulated HSAF. We verified the effectiveness of this correction method using data from observation sites not used in the aforementioned averaging in the frequency range from 0.12 to 15 Hz.
A new material particle dynamical domain decomposition method MPD3 has been developed. The method is suitable for a large scale parallel molecular dynamic simulation on a heterogeneous computing net. Performance of the MPD3 algorithm is tested in various computing environments, such as PC clusters, super computer clusters, and Grid. It is shown that the MPD3 algorithm is highly adaptivefor both computer clusters and Grid computing environments, even ifotherprograms are running on the same computer environment.
Tsunami evacuation simulations are often used to determine necessary countermeasures that will reduce human loss effectively after earthquakes and subsequent tsunamis. However, so far there has been no simulation for the estimated building damage using upto-date knowledge of seismic engineering. In this study, in order to clarify the effect of building damage on a tsunami evacuation, we first predicted building damage based on the nonlinear response analysis for a realistic strong ground motion and then simulated a tsunami evacuation considering road blockage due to the collapsed buildings. We used one district in Tanabe City in Wakayama Prefecture in Japan where we expect to have a 12 m of tsunami height after an earthquake along the Nankai Trough plate boundary. We found that the prepared capacity of evacuation sites is not enough to let everyone evacuate and that the number of survivors increases by 3-4% if all of the buildings and houses are seismically reinforced. Considering this, plus 1% of expected casualties inside the collapsed houses, it appears to be not as efficient to reinforce buildings and houses to prevent human loss in comparison with increasing the capacity of tsunami evacuation sites in the target district. However, the damage to building and houses will cause a lot of side effects which are not considered here, but will prolong the evacuation time. Thus, we concluded that we need to reinforce the buildings and houses as well as consider the appropriate placement, number, and capacity of the evacuation sites.
We first derived site amplification factors (SAFs) from the observed strong motions by the Japanese nationwide networks, namely, K-NET and KiK-net of National Institute of Earthquake Research and Disaster Resilience and Shindokei (Instrumental Seismic Intensity) Network of Japan Meteorological Agency by using the so-called generalized spectral inversion technique. We can use these SAFs for strong motion prediction at these observation sites, however, we need at least observed weak motion or microtremor data to quantify SAF at an arbitrary site. So we tested the capability of the current velocity models in Japan whether they can reproduce or not the observed SAFs at the nearest grid of every 250 m as the one-dimensional theoretical transfer functions (TTF). We found that at about one-half of the sites the calculated 1D TTFs show more or less acceptable fit to the observed SAFs, however, the TTFs tend to underestimate the observed SAFs in general. Therefore, we propose a simple, empirical method to fill the gap between the observed SAFs and the calculated TTFs. Validation examples show that our proposed method effectively predict better SAFs than the direct substitute of TTFs at sites without observed data.
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