Empirical predictive model for generating synthetic non-stationary stochastic accelerogram of the Iranian plateau: including far- and near-field effects as well as mainshock and aftershock categorization

Abstract

This work proposes comprehensive empirical predictive equations for generating stochastic synthetic 3-dimensional accelerograms for the Iranian plateau based on the existing database. First, the databank of Iranian accelerograms is collected, sorted, processed, declustered and categorized into the pulse-like and non-pulse-like data. To simulate the artificial accelerograms, a stochastic model capable of handling both the temporal and spectral non-stationarity of accelerograms is adopted. By implementing nonlinear curve fitting, parameters of the stochastic model are estimated. Then, the recorded events are categorized into eight distinct groups based on the existence of pulse-like components, and whether the recorded accelerogram is a mainshock or aftershock. Next, for each group, a Bayesian linear regression analysis is performed to obtain empirical predictive equations with probabilistic perspective. This proposed set of equations can estimate parameters of the stochastic model by using input seismological properties including event magnitude, focal depth, epicentral distance and average shear wave velocity of top 30 m of the subsurface profile. Finally, the validity of model is successfully assessed using the existing ground motion prediction equations for the Iranian plateau.