Stochastic Source Model for Strong Motion Prediction

Stochastic Source Model for Strong Motion Prediction

S. Sangeetha, S.T.G. Raghukanth
Copyright: © 2018 |Pages: 22
DOI: 10.4018/IJGEE.2018070101
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The article aims at developing a stochastic model which simulates spatial distribution of slip on the fault plane. This is achieved by analysing a large dataset of 303 finite-fault rupture models from 152 past earthquakes with varying fault mechanisms and in the magnitude range of 4.11-9.12. New scaling relations to predict the seismic source parameters such as fault length, fault width, rupture area, mean and standard deviation of slip have been derived for distinct fault mechanisms. The developed methodology models the spatial variability of slip as a two-dimensional von Karman power spectral density function (PSD) and correlation lengths are estimated. The proposed stochastic slip model is validated by comparing the simulated near-field ground response with the recorded data available for the 20th September 1999 Chi-Chi earthquake, Taiwan.
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1. Introduction

Engineers are more interested in the ground motion time histories which find its application in the earthquake resistant design of structures. This in turn necessitates reliable estimates of future ground motion (to ensure the safety of structures in future) which happens to be a challenging problem in earthquake engineering. Ground motion predictions can be carried out using empirical, numerical and analytical methods. Source mechanism models based on limited information of the earthquake source and the earth’s crustal structure can be used to simulate ground motions. However, these models require the earthquake forces to be specified in terms of spatial distribution of slip on the fault plane.

The problem of estimating the spatial variability of slip on the fault plane has been addressed by inverting ground motion records of past earthquakes (Hartzell et al. 1999; Ji et al. 2002; Raghukanth and Iyengar 2008). Researchers across the world work in this direction and come up with various finite-fault slip models after the occurrence of every event, which are documented in various literature. In some cases, multiple slip models are reported for the same event. This clearly explains the variability in earthquake source modeling. These are some of the popular websites ( where one can obtain finite-fault slip models of past earthquakes. Figure 1 depicts some of the existing finite-fault slip models. It can be observed from the figure that the slip models exhibit strong non-stationarity in their spatial distribution which cannot be modeled by deterministic functions. In such situations, stochastic approaches which require very few parameters provide an alternate way to characterize the slip field. Many efforts have been made by previous investigators along this direction (Wells and Coppersmith 1994; Somerville et al. 1999; Mai and Beroza 2002; Guatteri 2003; Lavallée et al. 2006; Raghukanth 2010). The major shortcoming with these models is that they were developed using a sparse database. In addition, the count of large events in their database is less. Due to the catastrophic nature of large magnitude events (Mw>7), engineers consider these as a serious threat. Recognizing the necessity of source models for large earthquakes, Raghukanth and Sangeetha (2014) have developed a stochastic model to estimate slip distribution for large events. Their slip database includes a total of 45 finite-fault rupture models from 33 large events. The model is capable of reproducing certain important features of the heterogeneous spatial variability observed in the slip. However, it lacks specific engineering application and validation. Besides, the varying focal mechanism has not been considered while deriving the scaling relations. Further, the stochastic model is limited to only large events. Apart from these, the existing empirical equations in literature needs to be revised with the availability of recent data to ensure accurate prediction of source characteristics. This exerts a major setback on the existing stochastic models. Thus, it is essential to develop a generalized stochastic source model which address the major research gaps pertaining to:

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