تعیین تابع سمی‌واریوگرام مؤلفه‌های قائم برای داده‌های شتاب‌نگاری ایران

نوع مقاله : Articles

نویسندگان

1 پژوهشگاه بین‌المللی زلزله‌شناسی و مهندسی زلزله

2 دانشگاه قم

3 موسسه آموزش عالی آل طه، تهران

چکیده

یکی از موضوعات مهم و به نسبت جدید، در طراحی لرزه‌ای سازه‌های با توزیع مکانی و شریان‌های حیاتی، توجه به مؤلفه قائم زلزله است. در این مطالعه، تابع سمی‌واریوگرام1 مؤلفه‌های قائم برای داده‌های شتاب‌نگاری ایران، جهت استفاده در همبستگی مکانی2 رکوردها و تحلیل خطر سازه‌های با توزیع مکانی، ارائه شد. اطلاعات مربوط به 220 زلزله ایران و تمامی ایستگاه‌های ثبت این زلزله‌ها مورد استفاده قرار گرفت. محاسبات برای پنج دوره تناوب سازه‌ای در محدوده 0 تا 3 ثانیه و با استفاده از معادله پیش‌بینی حرکت زمین مبتنی بر داده‌های مؤلفه قائم حرکت قوی ایران، انجام شد. جهت تخمین سمی‌واریوگرام تجربی از دو تخمین زننده کلاسیک و قوی و نیز به‌منظور برازش به داده‌ها از دو مدل نمایی3 و گودا استفاده گردید. برای معادله پیش‌بینی حرکت زمین4 سقراط و ضیایی‌فر [1]، مقادیر طول همبستگی (b) در مدل نمایی و مقادیر α و β در مدل گودا به‌دست آمد. با توجه به نتایج تجربی مشاهده شد که روند کلی طول همبستگی با افزایش دوره تناوب افزایش می‌یابد.Semi-Variogram Function for the Vertical Component of Iranian Acceleration Data  Hamid Zafarani1*, Seyed Mohammad Mehdi Ghafoori2 and Mahsa Shafiee3 1. Associate Professor of Earthquake Engineering, International Institute of Earthquake Engineering and Seismology, *Corresponding Author, e-mail: h.zafarani@iiees.ac.ir.2. Ph.D. Candidate, University of Qom, Iran3. M.Sc. Graduate, Ale-Taha Institute of Higher Education, Tehran, Iran The evaluation of potential human and economic losses arising from earthquakes, which may affect urban infrastructures that are spatially extended over an area, is important for national authorities, local municipalities, and the insurance and reinsurance industries. However, seismic-risk analysis of distributed systems and infrastructures need to apply a different approach with respect to the classical site-specific hazard and risk analysis. Ground motion intensity measures (IMs) and resulting structural responses are correlated in neighborhood sites. The correlation value depends on the distance between the adjacent sites and the natural vibration period of structures. In particular, when a lifeline system is of concern, classical site-specific hazard tools, which consider IMs at different locations independently, may not be accurate enough to assess the seismic risk. In fact, modeling of ground motion as a random field, which consists of assigning a spatial correlation to the IM of interest, is required. It is very common in the seismic design of spatially distributed structures and lifelines to include the correlation of the nearby earthquake records, through empirical semi-variogram functions. In this study, the semi-variogram of vertical components as a function of inter-site separation distance with respect to the ground motion prediction equations for the Iranian acceleration data (vertical peak ground acceleration (PGA) and vertical pseudo spectral acceleration (PSA)) are presented for the first time using acceleration data from 220 earthquakes. The calculations were carried out for five natural vibration periods in the range of 0 to 3 seconds and using ground motion prediction equations for vertical component. The selected ground motion prediction equation is the local model proposed by Soghrat & Ziaeifar (2017). For estimation of empirical semi-variogram, two classical and robust estimators, and to fit the data, the exponential and Goda models are used. For the ground motion prediction equation by Soghrat & Zyiaeifar (2017), the values of the range (b) in the exponential model and the values of α and β in the model of Goda (i.e. a continuous function fitted to experimental values in order to deduce semivariogram values for any possible site separation distance, Goda & Hong, 2008) are estimated. It is observed that the correlation trend range generally increases with period. Keywords: Semi-Variogram, Spatial Correlation, Vertical Component.

کلیدواژه‌ها


Soghrat, M.R. and Ziyaeifar, M. (2017) Ground motion prediction equations for horizontal and vertical components of acceleration in Northern Iran. Journal of Seismology, 21(1), 99-125.
Boore, D.M., Gibbs, J.F., Joyner, W.B., Tinsley, J.C., and Ponti, D.J. (2003) Estimated ground motion from the 1994 Northridge, California, earthquake at the site of the Interstate 10 and La Cienega Boulevard bridge collapse, West Los Angeles, California. Bulletin of the Seismological Society of America, 93(6), 2737-2751.
Esposito, S. and Iervolino, I. (2011) PGA and PGV spatial correlation models based on European multi-event datasets. Bulletin of the Seismological Society of America, 101(5), 2532-2541.
Goda, K. and Hong, H.P. (2008) Estimation of seismic loss for spatially distributed buildings. Earthquake Spectra, 24(4), 889-910.
Jayaram, N., and Baker, J.W. (2009) Correlation model for spatially distributed ground‐motion intensities. Earthquake Engineering & Structural Dynamics, 38(15), 1687-1708.
Sokolov, V., Wenzel, F., Jean, W.Y., and Wen, K.L. (2010) Uncertainty and spatial correlation of earthquake ground motion in Taiwan. Terr. Atmos. Ocean. Sci., 21, 905-921.
Park, J., Bazzurro, P., and Baker, J.W. (2007) Modeling spatial correlation of ground motion intensity measures for regional seismic hazard and portfolio loss estimation. Applications of Statistics and Probability in Civil Engineering, 1-8.
Goda, K. and Atkinson, G.M. (2009) Probabilistic characterization of spatially correlated response spectra for earthquakes in Japan. Bulletin of the Seismological Society of America, 99(5), 3003-3020.
Hong, H.P., Zhang, Y., and Goda, K. (2009) Effect of spatial correlation on estimated ground-motion prediction equations. Bulletin of the Seismological Society of America, 99(2A), 928-934.
Jayaram, N., and Baker, J.W. (2010) Considering spatial correlation in mixed-effects regression and the impact on ground-motion models. Bulletin of the Seismological Society of America, 100(6), 3295-3303.
Esposito, S. and Iervolino, I. (2012) Spatial correlation of spectral acceleration in European data. Bulletin of the Seismological Society of America, 102(6), 2781-2788.
Weatherill, G., Esposito, S., Iervolino, I., Franchin, P., and Cavalieri, F. (2014) ‘Framework for seismic hazard analysis of spatially distributed systems’. In SYNER-G: Systemic Seismic Vulnerability and Risk Assessment of Complex Urban, Utility, Lifeline Systems and Critical Facilities, pp. 57-88. Springer Netherlands.
Wagener, T., Goda, K., Erdik, M., Daniell, J., and Wenzel, F. (2016) A spatial correlation model of peak ground acceleration and response spectra based on data of the Istanbul Earthquake Rapid Response and Early Warning System. Soil Dynamics and Earthquake Engineering, 85, 166-178.
Zafarani, H., Soghrat, M.R. (2017) A selected dataset of the Iranian strong motion records. Natural Hazard, 86(3), 1307-1332.
Matheron, G. (1962) Traite de Geostatistique Appliquee. Vol. 1, Editions Technip.
Cressie, N. (1993) Statistics for Spatial Data. Revised Ed., Wiley, New York, 900 p.
Cressie, N. and Hawkins, D.M. (1980) Robust estimation of the variogram: I. Journal of the International Association for Mathematical Geology, 12(2), 115-125.
Goovaerts, P. (1997) Geostatistics for Natural Resources Evaluation. Oxford University Press on Demand.
Barnes, R.J. (1991) The variogram sill and the sample variance. Mathematical Geology, 23(4), 673-678.
Goda, K. and Hong, H.P. (2008) Spatial correlation of peak ground motions and response spectra. Bulletin of the Seismological Society of America, 98(1), 354-365.
Goda, K. and Atkinson, G.M. (2010) Intraevent spatial correlation of ground-motion parameters using SK-net data. Bulletin of the Seismological Society of America, 100(6), 3055-3067.
Journel, A.G. and Huijbregts, C.J. (1978) Mining Geostatistics. Academic Press, London, 600 p.
Zerva, A. and Zervas, V. (2002) Spatial variation of seismic ground motions: an overview. Applied Mechanics Reviews, 55(3), 271-297.
Bommer, J.J., Scherbaum, F., Bungum, H., Cotton, F., Sabetta, F., Abrahamson, N.A. (2005) On the use of logic trees for ground-motion prediction equations in seismic-hazard analysis. Bulletin of the Seismological Society of America, 95, 377-389.