Fig.2 Map showing the source model and FD simulation areas for the Nankai and the Tonankai earthquakes and site locations.

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1 Brief Introduction of Project III-3 Development of Simulation System and its Applications for Catastrophic Earthquake and Tsunami Disaster Response in Mega-cities Facing the Pacific : In terms of Strong Motion Prediction for The Tonankai and Nankai Earthquake Kojiro Irikura 1, Sumio Sawada 1, Hidenori Kawabe 2, Katsuhiro Kamae 2, Kazuhito Hikima 3, Tetsu Masuda 3, Nozomu Yoshida 3, Nobuo Fukuwa 4, Jun Tobita 4, Masaru Nakano 4 and Hiroaki Kojima 4 1 Disaster Prevention Research Institute, Kyoto University, Uji , Japan 2 Research Reactor Institute, Kyoto University, Kumatori , Japan 3 Earthquake Engineering Center, Oyo Corporation, Saitama, , Japan 4 Graduate School of Environmental Studies, Nagoya University, Nagoya , Japan Summary The project III-3 of the special project for earthquake disaster mitigation in urban areas, named as Development of simulation system and its applications for catastrophic earthquake and tsunami disaster response in mega-cities facing the Pacific, is running from The project includes a sub-project for prediction of strong ground motion from the Tonankai and Nankai earthquake. The sub-project has 3 themes as follows; (a) Method for predicting strong ground motion from a huge earthquake, (b) Study on strong ground motion prediction for wide area and its verification, (c) Development of Precise Ground-Structure Modeling System for Strong Motion Prediction in Large-scale Sedimentary Plain. The results for these themes are shown briefly in this paper. 1. Introduction The project III-3 of the special project for earthquake disaster mitigation in urban areas, named as Development of simulation system and its applications for catastrophic earthquake and tsunami disaster response in mega-cities facing the Pacific, is running from The project includes following sub-projects; (1) Simulation of strong ground motion and its application to disaster mitigation, (2) Development of simulation methods to evaluate seismic performance of large scale lifeline networks and technologies to strengthen them, (3) Estimation of catastrophic tsunami damage and development of reduction strategy, (4) Participatory decision making system for disaster mitigation strategies based on an integrated earthquake simulator, (5) Improvement of disaster management capability using earthquake disaster response simulator based on the New Public Management framework, (6) Seven related studies on disaster response strategy. The sub-project (1) concerns the prediction of strong motion. The sub-project has 3 themes as follows; (a) Method for predicting strong ground motion from a huge earthquake (by Kyoto University), (b) Study on strong ground motion prediction for wide area and its verification (by Oyo Corporation), (c) Development of Precise Ground-Structure Modeling System for Strong Motion Prediction in Large-scale Sedimentary Plain (by Nagoya University). We show summaries of the results for these themes in the following sections. 2. Method for predicting strong ground motion from a huge earthquake (i) Scaling law for huge earthquakes Source-scaling relations derived from dynamic rupture simulations are developed. Our simulations consist of pure-strike sub-surface earthquakes when L less than W and surface earthquake when L larger or equal than W, where L and W are length and width of the fault, respectively. Wmax is assumed to be 20km that represents the brittle crust of the earth. The fault models are calculated up to L=20Wmax. The crack models are assumed to have constant stress drop over the fault and for the asperity models the stress drop is on the asperity area only and in the background area is zero (the results of asperity model are not shown here). 17

For sub-surface earthquakes the fault is located 3km depth. Fault models with fault area (LxW) 12.5x12.5, 17 x 17 and 20 x 20 in Km2 are used.

The length (L) of the faults in km are L= 20, 40, 60, 80, 100, 200, 300, 400. Models with stress drop ( σ) equals to 2.3, 3.0, 5 and 10.0MPa are used for surface and sub-surface earthquakes.

85 Vs. The slip-weakening model with Dc=0.2m was used as friction law during rupture process. The source scaling models implied from dynamic rupture simulation of crack faults, as shown in Fig.

2 For sub-surface earthquakes the fault is located 3km depth. Fault models with fault area (LxW) 12.5x12.5, 17 x 17 and 20 x 20 in Km2 are used. For surface earthquakes the faulting breaks the free-surface and all the faults have a width (W) equals to 20km, corresponding to the maximum W that represents the brittle crust of the earth. The length (L) of the faults in km are L= 20, 40, 60, 80, 100, 200, 300, 400. Models with stress drop ( σ) equals to 2.3, 3.0, 5 and 10.0MPa are used for surface and sub-surface earthquakes. The medium with free surface is assumed to have P wave velocity, S wave velocity, density and Q values respectively as follow: Vp = 6.0km/s, Vs=3.5km/s, r=2700kg/m3, Q=400. The rupture velocity = 0.85 Vs. The slip-weakening model with Dc=0.2m was used as friction law during rupture process. The source scaling models implied from dynamic rupture simulation of crack faults, as shown in Fig.1, explain the three-lineal empirical relation proposed by Irikura and Miyake (2001) for the following condition of stress drop: -When L<1.5Wmax, stress drop remains constant with σ = 1.89MPa -When 1.5Wmax<L<10Wmax, stress drop vary from 1.89MPa to 7.5 MPa. -When L>10Wmax, stress drop remains constant with σ = 7.5MPa. The results corresponding to the blue and black dashed lines of Fig.1 was predicted using the relationship between the ratios of seismic moment and stress drop for the same fault area, as Mo 1 / Mo 2 σ 1 / σ 1. (ii) Prediction of Strong Ground Motion for the Nankai and the Tonankai Earthquakes The huge subduction earthquakes along the Nankai trough have the high potential for the next occurrences. In this study, we try to predict strong ground motions from the hypothetical Nankai (M8.4) as well as the Tonankai (M8.1) earthquakes based on the recipe for strong ground motion prediction from scenario earthquakes proposed by Irikura et al. (2003). In particular, we concentrate the prediction of strong ground motions inside the Osaka basin with very complicated underground structure. Broad-band strong ground motions have been estimated by the empirical Green s function method. Furthermore, the characteristics of long period ground motions have been investigated by the 3-D finite difference method considering 3-D underground structure of the Osaka basin. Fig.2 Map showing the source model and FD simulation areas for the Nankai and the Tonankai earthquakes and site locations. Fig.1 Relationship between seismic moment and rupture area for different stress drop of crack models after dynamic rupture simulation. The red line is proposed by Irikura and Miyake(2001). The circles are observations from real earthquakes. We basically use the characterized source models proposed by the Headquaters for Earthquake Research Promotion in Japan (HERP). Such models are constructed following the recipe for strong ground motion prediction by Irikura et al. (2003). Fig.2 shows the source model composed of three asperities on the fault for the Nankai and the Tonankai earthquakes, together with the region of the finite difference simulation, the epicenter of the earthquakes (EGF-1 and EGF-2) used as the empirical Green s functions and the site locations. The main source parameters for each earthquake are summarized in Table 1. 18

Table 1 Source parameters for the Nankai and the Tonankai earthquakes.

The amplitude of the ground motions for the linked earthquake of the Nankai and Tonankai earthquakes becomes slightly larger than the individual

5 shows the synthesized ground motions and these pseudo velocity response spectra at 3 sites (FKS, MRG, YAE) located inside the Osaka basin for the

These results show the necessity of the more advanced prediction of long period ground motions in mega-city Osaka located inside the Osaka basin.

3 Table 1 Source parameters for the Nankai and the Tonankai earthquakes. period of the pseudo velocity response spectra is about 5 second and the amplitude is larger than 140 cm/s. The amplitude of the ground motions for the linked earthquake of the Nankai and Tonankai earthquakes becomes slightly larger than the individual earthquake. Fig.5 shows the synthesized ground motions and these pseudo velocity response spectra at 3 sites (FKS, MRG, YAE) located inside the Osaka basin for the Tonankai earthquake. The predominant periods of the synthetic motions inside basin are 4 to 8 seconds. These results show the necessity of the more advanced prediction of long period ground motions in mega-city Osaka located inside the Osaka basin. Fig.3 Predicted velocity motions at OSA site in cases of the individual occurrence of the Nankai and the Tonankai earthquakes and the linked occurrence of both earthquakes. Fig.5 Pseudo velocity response spectra of the predicted ground motions for the Tonankai earthquake (Case-1) at 3 sites (FKS, MRG, YAE) located inside the Osaka basin. Fig.6 The top depth of the sediment basement Fig.4 Pseudo velocity response spectra of the predicted ground motions at OSA site. Table 2 Physical parameters of each sediment layer and bedrock. The synthesized ground motion (0.1 to 10.0Hz) and the pseudo velocity response spectra at OSA in Osaka city are shown in Fig.3 and Fig.4. The duration of the ground motions are longer than 300 seconds. The peak 19

Tonankai Case-1 Tonankai Case-2 Nankai Period=4sec Period=7sec Fig.

(NS Component) We estimate long period ground motions using the 3-D finite difference method (Pitarka, 1999) with the 3-dimentional underground structure models for the Osaka basin by Miyakoshi et al.

We assumed that the bedrock outcrops outside the Osaka basin. We calculated velocity seismograms inside the basin from only asperity.

4 Tonankai Case-1 Tonankai Case-2 Nankai Period=4sec Period=7sec Fig. 7 Distributions of the pseudo velocity response spectral amplitude in two periods of 4 sec and 7 sec by the finite difference method. (NS Component) We estimate long period ground motions using the 3-D finite difference method (Pitarka, 1999) with the 3-dimentional underground structure models for the Osaka basin by Miyakoshi et al. (1999) and Horikawa et al. (2002). The top depth of the sediment basement is depicted in Fig.6, and the parameters of the sediment layers and bedrock are summarized in Table 2. We assumed that the bedrock outcrops outside the Osaka basin. We calculated velocity seismograms inside the basin from only asperity. We used two kinds of source models assuming different starting point for the Tonankai earthquake. Fig.7 shows synthetic pseudo velocity responses of period 4 and 7 seconds in cases of the Nankai earthquake and the Tonankai earthquake. From these figures, the characteristics of the long period ground motions inside the basin are changed by the source location as well as the rupture process. It is concluded that. in mega-city Osaka located inside basin, the long period ground motions (4~8 seconds) with very long duration have been predicted for both huge earthquakes. The amplitude of the predicted response spectrum is over the design spectrum around period of 5 sec. These results suggest that we need to investigate the seismic safety of high-rise buildings and base-isolated buildings and so on. Furthermore, we need to increase the accuracies of the predicted long period ground motions inside the Osaka basin. 3. Study on strong ground motion prediction for wide area and its verification (i) Strong ground motion prediction for wide area Strong ground motion prediction for a wide area and at a high density requires the followings; (1) Detailed modeling and verification of deep and shallow structures of elastic wave velocity, density and Q value based on geo-technical data, borehole data and geology. (2) Modeling and verification of seismic source in terms of asperity distribution. Modeling of subsurface structure and seismic source adequate to strong ground motion prediction has just started. An example is series of recent studies carried out by the Specialists Committees under the Central Disaster Management Council for the Tokai, Tonankai, and Nankai earthquakes. They modeled and verified the deep and shallow subsurface structures ranging from the Kanto to the Kyushu region (Fig.8 and Fig.9), estimated asperity locations of seismic sources based on the historical data of JMA intensity scale, and presented the distribution of JMA intensity scale for each 1 km by 1 km mesh in wide areas (Fig.10). Verification of the predicted results by the historical data is important from the view point of forming consent on the disaster countermeasures for the forthcoming event as well as is useful for the source process study of past events. The asperity location in detail is iteratively adjusted and determined through comparison between the calculated results and actual data. We have attempted to estimate the initial guess of asperity location by inversion of JMA intensity scale distribution. The earthquakes analyzed are the Hoei earthquake in 1707, the Ansei Tokai and Ansei Nankai earthquakes in 1854, the Showa Tonankai earthquake in 1944, and the Showa Nankai earthquake in

The JMA intensity scale is converted to the maximum ground velocity by the empirical relationship, and reduced to the value on the base rock according to the shallow subsurface structure. Fig.

11, that the inversion of historical intensity scale data successfully estimates the asperity location of the earthquake, and that the asperity location by the Central Disaster Management Council is

Green squares represent asperities. 36 35 34 7.8 7.4 7.0 33 6.6 6.2 5.8 5.4 100 km 5.0 4.6 32 4.2 132 133 134 135 136 137 138 139 140 Fig.11 Result of inversion.

5 The JMA intensity scale is converted to the maximum ground velocity by the empirical relationship, and reduced to the value on the base rock according to the shallow subsurface structure. Fig.11 shows the inversion results for the Hoei earthquake in It is seen, from the Fig.10 and Fig.11, that the inversion of historical intensity scale data successfully estimates the asperity location of the earthquake, and that the asperity location by the Central Disaster Management Council is in accordance with the inversion results obtained here. Fig.8 shallow structure (distribution of AVS30) Fig.9 deep structure (altitude of Vs 3,000m/s layer). Fig.10 Distribution of predicted JMA intensity scale. Green squares represent asperities km Fig.11 Result of inversion. Red colored regions indicate asperities. (ii) Effect of damping term for analyzing non-linear ground response In many earthquake response analysis of ground, Rayleigh damping has been used, by which damping matrix [C] is developed by a linear combination of mass matrix [M] and stiffness matrix [K] as [ C] = α[ M] + β[ K] (1) where α and β are parameters. The damping constant h by Rayleigh damping is given as α βωi hi = + (2) 2ω i 2 where subscript i denotes mode and ω is a circular frequency. Equation (2) indicates that a stiffness proportional term is important in the practice of numerical analysis because it gives large damping at high frequency region; therefore, suppress high frequency unusual response such as pulse as typically seen in Fig.12 that may result in instability of numerical analysis. It is seen that Rayleigh damping succeeds to erase pulse without changing overall behavior. Case studies are carried out in order to see how damping term affects dynamic response by using 5 soil profiles that are typical soil profile in the Kanto district. Fig.13 shows an example of peak acceleration and displacement. The value of β is determined so that damping at first predominant period becomes specified value in the figure, i.e., 1, 3 or 5 %. Peak displacement Displacement becomes smaller as damping ratio increases. Accelerations are also affected, but its effect is not large at the ground surface because of the nonlinear behavior between GL-9.5 and 16 m. The same tendency was observed in all soil profiles. In order to improve this shortage, a modal damping is alternately incorporated, which is expressed as [ C] = [ M][ ξ] 2h 1/ [ ] T jω j mj ξ [ M] (3) where [ξ] is eigen vectors and two matrices in the middle of Eq. (3) are diagonal matrices whose diagonal component are 2η i ω i and inverse of generalized mass. Obviously this damping has more 21

practice and 2) it is large value for the stability of numerical analysis.

14 shows contour lines of maximum strain in the layer where shear strain was largest.

It is clearly seen that shear strain is smaller as boundary frequency is smaller and as damping ratio is larger.

) Example of pulse wave Target Rayleigh Constant 40 4.

$existing subsurface exploration data, especially reflection and refraction survey, microtremor array, boring, P-S logging and gravitational$ anomalies as shown in Figs.15 and 16 (Fukuwa et al. 2000).

anomalies as shown in Figs.15 and 16 (Fukuwa et al. 2000).

6 freedom than Rayleigh damping. Then, damping ratio are determined in such a way that 1) it has physical meaning in frequency regions that is important in the engineering practice and 2) it is large value for the stability of numerical analysis. Since the former term is not well known at present, no damping is considered. The damping at high frequency is set constant. Fig.14 shows contour lines of maximum strain in the layer where shear strain was largest. Here, abscissa is a boundary frequency between two regions and ordinate is damping ratio at the high frequency region. It is clearly seen that shear strain is smaller as boundary frequency is smaller and as damping ratio is larger. Therefore, a relevant set can be chosen to make the earthquake response accurate. Acceleration (m/s 2 ) Fig Time (sec.) Example of pulse wave Target Rayleigh Constant Development of Precise Ground-Structure Modeling System for Strong Motion Prediction in Large-scale Sedimentary Plain (i) Web System for Ground-Structure Survey A webgis to estimate the dynamic soil model in the Nobi plain is constructed, which conjugates maximally all of the existing subsurface exploration data, especially reflection and refraction survey, microtremor array, boring, P-S logging and gravitational anomalies as shown in Figs.15 and 16 (Fukuwa et al. 2000). The system estimates the soil velocity and density layering structure along an arbitrary section line and evaluates the soil amplification at an arbitrary site. The 3-dimensional subsurface structure is drawn by VRML (Vertual Reality Modeling Language) as shown in Fig.17 which make possible to visualize complex surface layers from arbitrary point of view on an ordinary browser software. Depth (m) Max. Acceleration (m/sec 2 ) Max. Displacement (cm) m/s 2 Fig.13 Peak response value 1% 3% 5% Fig.15 Deep subsurface exploration in Nobi plain. Fig.16 Site and data organized by WebGIS. Fig.14 Contour of maximum shear strain 22

et al. 2001.) and the Nagoya University Soil-Structure Seismic Observation Network System (NUS-Net).

7 Fig.17 Subsurface 3-d modeling shown by VRML. (ii) Web System for Observed Earthquake Records on Ground The data collection/publishing system developed by the authors consists of the Tokai Area Seismic Observation Network System (TAS-Net, Tobita et al ) and the Nagoya University Soil-Structure Seismic Observation Network System (NUS-Net). TAS-Net is a super-network composed of multiple networks controlled by several authorities in the Tokai region, the third largest urban area in Japan. It collects the seismic waveforms in the soil surface and underground and publishes the data in a unified database over the Internet. It is essential to learn the characteristics of high-intensity seismic vibrations in each region in order to make the most logical choices in the seismic design of buildings. This data must be recorded during earthquakes. The Tokai region, the third largest metropolitan region in Japan and home to 10 million inhabitants, encompasses a number of plains: the Nobi Plain, the Ise Plain, the Okazaki Plain and the Toyohashi Plain. Organizations of many authorities are carrying out observations of earthquakes in this region, including several local political bodies, one electric power company, one gas company, one public corporation operating the expressways, several universities, and others. Each organization places its earthquake observation network on-line and creates an environment permitting access to all the observation data; this allows effective exploitation of the data. As each organization had established its database in accordance with its own purposes prior to the merger, many were required to re-create the data storage system to comply with the merger while continuing to meet the original purposes of the observations. Further, each organization had a different transmission system and different server for data collection, and it proved to be no easy task to unify data display. It was agreed that this super-system would send data requests to the organizations in the middle of the night, at least half a day after the occurrence of a seismic event so as to minimize disruption to the organizations operations. Each organization established an interface such that no changes to systems were necessary in order to connect to the super-network. The super-network initially combined seismic event observation networks managed by 3 prefectures, 2 cities, 3 companies and 3 universities. As shown in Fig.18, it was comprised of over 300 data collection points. It began operation as a super-network in Other organizations have joined the network since then. The data records consist of the wave shapes and response spectra, as indicated in Fig.19, as well as seismometer installation, soil data, and seismometer characteristics. This information is published on the web. Comprehensive analysis of abundant data recorded over a large observation area have lead to a better understanding of the dynamic characteristics of the soil throughout the region. This represents valuable information for the design of high-rise buildings and base-isolated structures. Fig.18 Observation sites of TAS-Net. Fig.19 Observed data characteristics on browser. (iii) Web System for Observed Earthquake Records on Structures NUS-Net records a wealth of data from multiple buildings and underground sensors on the Nagoya University campus over the campus LAN. In addition to the waveform data, this system also publishes detailed data on building structures, the geological structures beneath the university, and other material. A 23

strategy was conceived for choosing the buildings to be observed in order to obtain the best illustration of the factors affecting the behavior of buildings during earthquakes (Kojima et al. 2003).

Off campus, three houses, two buildings with seismic isolation, one temple, and two governmental buildings are also under observation.

20 and 21, the records are in a form suitable for publishing and are made available on the Internet.

always available via the Internet. It is also possible to download digitalized records of confirmed observation data.

8 strategy was conceived for choosing the buildings to be observed in order to obtain the best illustration of the factors affecting the behavior of buildings during earthquakes (Kojima et al. 2003). Observations of earthquakes are currently being carried out or are scheduled to begin soon at the 13 structures. These structures are on the Higashiyama and Tsurumai campuses. Off campus, three houses, two buildings with seismic isolation, one temple, and two governmental buildings are also under observation. The seismometers in the Nagoya University buildings are interfaced to the university LAN. As shown in Figs.20 and 21, the records are in a form suitable for publishing and are made available on the Internet. The concept diagrams, structural drawings, soil data, observation point locations, sensor specifications, list of observed earthquakes, seismic waveforms, microtremor records, and other data are always available via the Internet. It is also possible to download digitalized records of confirmed observation data. HTML programs are also available to ease the task of publishing seismic data on the web for other institutions. Fig.20 Observed records on a building for various earthquakes(left). Digital Data Download(right). soil, seismic ground motion and microtremor, and grouping of dynamic ground properties in Nagoya city area, AIJ J. Technol. Des. 10, (in Japanese). Horikawa, H., K. Mizuno, K. Satake, H. Sekiguchi, Y. Kase, Y. Sugiyama, H. Yokota, M. Suehiro and A. Pitarka (2002). Three-dimensional subsurface structure model beneath the Osaka Plain, Annual Report on Active Fault and Paleoearthquake Researches, Geological Survey of Japan/AIST, No. 2 (in Japanese with English abstract). Irikura, K. (1986). Prediction of strong acceleration motion using empirical Green s function, Proc. 7th Japan Earthquake Symp., Irikura, K. and Miyake, H.(2001). Prediction of Strong Ground Motions for Scenario Earthquakes, Journal of Geography, 110(6) , 2001, (in Japanese with English abstract). Irikura, K., H. Miyake, T. Iwata, K. Kamae, H. Kawabe, and L. A. Dalguer (2003). Recipe for predicting strong ground motion from future large earthquake, Annuals of Disaster Prevention Research Institute, Kyoto University, No.46B, , (in Japanese with English abstract). Kojima, H., N. Fukuwa, J. Tobita, M. Nakano (2003). Development of web system to open seismic observation data of buildings, AIJ J. Technol. Des. 17, (in Japanese). Miyakoshi, K., T. Kagawa, B. Zhao, T. Tokubayashi and S. Sawada (1999). Modeling of deep sedimentary structure of the Osaka Basin (3), Proc. 25th JSCE Eqrthq. Eng. Symp., (in Japanese with English abstract). Pitarka, A., K. Irikura, T. Iwata, and H. Sekiguchi (1998). Three-dimensional simulation of the near-fault ground motion for the 1995 Hyogo-ken Nanbu (Kobe), Japan, earthquake, Bull. Seism. Soc. Am., 88, Tobita, J., N. Fukuwa, M. Nakano and K. Yamaoka (2001). Development of on-line data acquisition system for strong motion seismic records and its application to existent observation systems, AIJ J. Technol. Des. 13, (in Japanese). Fig.21 Specification of building(left). Boring log for surface geology(right). References Fukuwa, N., J. Tobita, M. Nakano, H. Takahashi, M. Iida and R. Ishida (2000). Compilation of data of 24

STRONG GROUND MOTION PREDICTION FOR HUGE SUBDUCTION EARTHQUAKES USING A CHARACTERIZED SOURCE MODEL AND SEVERAL SIMULATION TECHNIQUES

13 th World Conference on Earthquake Engineering Vancouver, B.C., Canada August 1-6, 24 Paper No. 655 STRONG GROUND MOTION PREDICTION FOR HUGE SUBDUCTION EARTHQUAKES USING A CHARACTERIZED SOURCE MODEL