Strong Ground Motion Prediction for Scenario Earthquakes

Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

Strong ground motion is the most basic information to estimate seismic damage and examine the earthquake-resisting capacity of buildings. It’s very important to predict strong ground motions, estimate seismic damage and conduct earthquake countermeasures for a future large earthquake. Firstly, three basic components of seismic motions, i.e., source, path and site characteristics are mentioned to understand essence of method for strong ground motion prediction. Secondary, methods of strong ground motion prediction based on fault rupture propagation model such as empirical Green’s function method and stochastic Green’s function method are explained. In empirical Green’s function method, seismic motion from large earthquake is synthesized using that from small earthquake according to scaling laws of spatial and temporal growth of fault rupture. These are scaling laws of fault parameters for small and large earthquakes and the omega-squared source spectra. When there is no suitable observed record as Green’s function, stochastic Green’s function method can be adopted to predict strong ground motions. Instead of observed seismic motions from small earthquake, the method uses simulated motions as Green’s function and synthesizes seismic motions from a large earthquake using the same concepts of empirical Green’s function method. Thirdly, recipe for predicting strong ground motion from future large earthquakes is introduced. The recipe is summarized standard methodology for prediction of strong ground motions. The broadband strong ground motions can be predicted accurately by applying the recipe. Moreover, theoretical and empirical methods to evaluate path and site characteristics are explained. Finally, the stochastic Green’s function method is demonstrated on example of strong ground motion prediction for existing active fault in Iran.

Keywords

Strong ground motion prediction Source–path–site characteristics Empirical Green’s function method Stochastic Green’s function method Recipe for predicting strong ground motions 

Notes

Acknowledgments

This chapter is presented with reference to some chapters written by emeritus Prof. Irikura in Kyoto University. I express gratitude to Prof. Irikura. The clarity and completeness of this chapter was improved by comment from Prof. Kagawa in Tottori University and Dr. Petukhin in Geo-Research Institute. The project for predicting strong ground motions from future large earthquake at Tabriz Bazaar was supported in part by the grant-in aid for Science Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan (No. 21254001). Prof. Miyajima in Kanazawa University and Dr. Fallahi in Azerbaijan University of Shahid Madani lead the project energetically. I’m grateful to Prof. Miyajima and Dr. Fallahi. Ground structure model at Tabriz bazaar constructed by Dr. Yoshida in Fukui National College of Technology, and Dr. Solitani Jigheh in Azerbaijan University of Tarbiat Moallem. Generic mapping tool by Wessel and Smith (1998) was used for making several figures.

References

  1. Aki K (1967) Scaling law of seismic spectrum. J Geophys Res 72:1217–1231CrossRefGoogle Scholar
  2. Boore DM (1983) Stochastic simulation of high-frequency ground motion based on seismological models of the radiated spectra. Bull Seismol Soc Am 73:1865–1894Google Scholar
  3. Chun KY, West GF, Kokoski RJ, Samson C (1987) A novel technique for measuring L g attenuation: results from Eastern Canada between 1 to 10 Hz. Bull Seismol Soc Am 77:398–419Google Scholar
  4. Hessami K, Jamali F, Tabassi H (2003) Major active faults of Iran. International institute of earthquake engineering and seismologyGoogle Scholar
  5. Hartzell SH (1978) Earthquake aftershocks as Green’s functions. Geophys Res Lett 5:1–4CrossRefGoogle Scholar
  6. Headquarters for Earthquake Research Promotion (Director: Ministry of Education, Culture, Sports, Science, and Technology) (2008) Strong ground motion prediction method (“Recipe”) for earthquakes with specified source faults. http://www.jishin.go.jp/main/index-e.html (in Japanese)
  7. Irikura K (1986) Prediction of strong acceleration motion using empirical Green‘s function. Proceedings of the 7th Japan earthquake engineering symposium, pp 151–156Google Scholar
  8. Irikura K, Kagawa T, Sekiguchi H (1997) Revision of the empirical Green’s function method by Irikura (1986). Programme and abstracts, seismological society of Japan, 2, B25 (In Japanese)Google Scholar
  9. Irikura K, Miyake H (2001) Prediction of strong ground motions for scenario earthquakes. J Geogr 110:849–875 (In Japanese with English abstract)CrossRefGoogle Scholar
  10. Irikura K, Miyake H, Iwata T, Kamae K, Kawabe H, Dalguer LA (2004) Recipe for predicting strong ground motions from future large earthquakes. Proceedings of the 13th world conference on earthquake engineering, Paper No. 1371, Vancouver, CanadaGoogle Scholar
  11. Irikura K, Kurahashi S (2008) Validity of strong motion prediction recipe for inland-crust earthquakes. Proceedings of the 14th world conference on earthquake engineering, Beijing, ChinaGoogle Scholar
  12. Iwata T, Irikura K (1988) Source parameters of the 1983 Japan sea earthquake sequence. J Phys Earth 36:155–184CrossRefGoogle Scholar
  13. Kamae K, Irikura K, Fukuchi Y (1991) Prediction of strong ground motion based on scaling law of earthquake−by stochastic synthesis method. J Str Constr Eng (Trans AIJ) 430:1–9 (In Japanese with English abstract)Google Scholar
  14. Kamae K, Irikura K (1998) Source model of the 1995 Hyogo-Ken Nambu earthquake and simulation of near source ground motion. Bull Seismol Soc Am 88:400–412Google Scholar
  15. Kanamori H, Anderson DL (1975) Theoretical basis of some empirical relations in seismology. Bull Seismol Soc Am 65:1073–1095Google Scholar
  16. Matsuzawa T, Hasegawa A, Takagi A (1984) Estimation of Q-factor by double spectral ratio method. Proceedings of the annual meeting of seismological society of Japan, 2:C75, 247 (In Japanese)Google Scholar
  17. Petukhin A, Tsurugi M, Abdolhossein F, Miyajima M (2011) Receiver function method for estimation of the shallow structure: example for Tabriz, Iran, Japan Geoscience Union Meeting 2012, HDS004-P09Google Scholar
  18. Somerville PG, Irikura K, Graves R, Sawada S, Wald D, Abrahamson N, Iwasaki Y, Kagawa T, Smith N, Kowada A (1999) Characterizing crustal earthquake slip models for the prediction of strong ground motion. Seismol Res Lett 70:59–80CrossRefGoogle Scholar
  19. Sato N, Yonezaki F, Harita K, Tsurugi M, Kagawa T, Toki K (2001) Strong motion simulation at dam site during the 2000 Tottori-Ken Seibu earthquake. Proceedings of the 26th JSCE earthquake engineering symposium, Sapporo, pp 341–344 (in Japanese)Google Scholar
  20. Schnabel PB, Lysmer J, Seed HG (1972) SHAKE a computer program for earthquake response analysis of horizontally layered sites. EERC 72–12Google Scholar
  21. Sugito M (1993) Frequency-dependent equivalent strain for earthquake response analysis of soft ground. Proceedings of the 3rd Republic of China and Japan joint seminar on natural hazards mitigation, Tainan, pp 409–422Google Scholar
  22. Tsurugi M, Tai M, Kowada A, Tatsumi Y, Irikura K (2000) Estimation of empirical site amplification effects using observed records. Proceedings of the 12th world conference on earthquake engineering, 1243Google Scholar
  23. Tsurugi M, Kagawa T, Irikura K (2008) Study on high-cut frequency characteristics of ground motions for inland crustal earthquakes in Japan. Proceedings of the 14th world conference on earthquake engineering, Beijing, 02-0036Google Scholar
  24. Wessel P, Smith WHF (1998) New, improved version of generic mapping tools released. Am Geophys Un, EOSGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Geo-Research InstituteNishi-kuJapan

Personalised recommendations