3:45 PM - 4:00 PM
[SCG59-08] Drawig of the realtime seismic intesity on live screen and maximum distribution map for the Earthquak disaster prevension.
-Planar indication of seismic intensity using surface ground amplification factor-
Keywords:Local government measurement intensity meter, Engineer basement seismic intensity , Evaluate by surface amplification factor , Real time seismic intensity, Live view, Maximum seismic intensity distribution map
Earthquake damage can be caused from a small-sized earthquake (magnitude class 4.5) occurs in inland areas to a large-sized earthquake such as the 2011 Tohoku earthquake (Mw 9.1). In recent years, it is possible to estimate the amount of detailed displacement of the destruction zone from the inversion analysis of observed data. Moreover, ground motion prediction equations (GMPEs) and earthquake prediction recipes have been proposed. Those developments contributed to the earthquake disaster mitigation measures. Earthquake early warning system (EEW) of Japan has been in operation since 2007, it is very useful to issue information that have a great impact on disaster mitigation, however, some problems still exist with the information issued by EEW. For instance, the warnings are sometimes issued after S-wave arrivals, which leaves an area without alarm known as a blind zone area.
In this study, we used a dense seismic network dataset recorded during the 2016 Kumamoto (Mw 7.0), and the 2019 January 3rd (Mw 5.1) earthquakes, maximum JMA intensity of 7 and 6 upper, respectively. In spite of the difference of magnitudes and source size area the two earthquakes had close maximum intensity values. It noted that the average spacing distribution of seismic instrument in that regions is ~ 4-5 km. We here obtain the maximum peak ground acceleration (PGA) on free surface on each observed point, we then corrected PGA to engineering basement, using site effect information from J-SHIS (NIED), spaced by 1×1 km2. Using PGA on engineering bedrock, we estimate the PGA on free surface by multiplying with surface amplification factor at each grid point of 1×1 km2, we then evaluate the seismic intensity at each grid. We repeat this process for each observed point considering the inter-station distance and finally produce the intensity map.
Our method gives a quick information about earthquake location (close or far), hypocentral depth (shallow or deep) inferred from apparent velocity distribution maps. It also recognizes the earthquake source size (small or large) inferred from the spatial distribution of PGA. We also attempted scaling by so-called P wave detection and maximum seismic intensity distribution prediction using theoretical amplitude ratio (5 times) of S wave / P wave at the seismic source.
It is a very important issue to observe the strong-ground motion in real-time to improve the performance of EEW. We strongly recommend local government to have their own high density network, which gives a quick earthquake disaster information. The Japan Meteorological Agency will aim for speeding up and improving the precision of emergency earthquakes (eliminating the blind zone) by integrating server information of each local government (with small budget without directly telemetering observation point data covering several thousands of points).
Acknowledgments
We express our appreciations to the officials in Tokyo, Kyoto, Nara, Sapporo,
Kumamoto, Miyagi, Fukushima and Ibaraki prefectures uploaded the seismic intensity data used for the analysis to the Japan Meteorological Agency Web page.
In this study, we used a dense seismic network dataset recorded during the 2016 Kumamoto (Mw 7.0), and the 2019 January 3rd (Mw 5.1) earthquakes, maximum JMA intensity of 7 and 6 upper, respectively. In spite of the difference of magnitudes and source size area the two earthquakes had close maximum intensity values. It noted that the average spacing distribution of seismic instrument in that regions is ~ 4-5 km. We here obtain the maximum peak ground acceleration (PGA) on free surface on each observed point, we then corrected PGA to engineering basement, using site effect information from J-SHIS (NIED), spaced by 1×1 km2. Using PGA on engineering bedrock, we estimate the PGA on free surface by multiplying with surface amplification factor at each grid point of 1×1 km2, we then evaluate the seismic intensity at each grid. We repeat this process for each observed point considering the inter-station distance and finally produce the intensity map.
Our method gives a quick information about earthquake location (close or far), hypocentral depth (shallow or deep) inferred from apparent velocity distribution maps. It also recognizes the earthquake source size (small or large) inferred from the spatial distribution of PGA. We also attempted scaling by so-called P wave detection and maximum seismic intensity distribution prediction using theoretical amplitude ratio (5 times) of S wave / P wave at the seismic source.
It is a very important issue to observe the strong-ground motion in real-time to improve the performance of EEW. We strongly recommend local government to have their own high density network, which gives a quick earthquake disaster information. The Japan Meteorological Agency will aim for speeding up and improving the precision of emergency earthquakes (eliminating the blind zone) by integrating server information of each local government (with small budget without directly telemetering observation point data covering several thousands of points).
Acknowledgments
We express our appreciations to the officials in Tokyo, Kyoto, Nara, Sapporo,
Kumamoto, Miyagi, Fukushima and Ibaraki prefectures uploaded the seismic intensity data used for the analysis to the Japan Meteorological Agency Web page.