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Regional stochastic GMPE with available recorded data for active region – Application to the Himalayan region
Abstract New ground motion prediction equation for the active Himalayan region for a wide range of moment magnitude ( 4–9) and distance (10–750 km) is developed. For simulating the synthetic ground motions; source, path, and site terms are derived using the Fourier amplitude spectrum of the recorded ground motion data. Uncertainty of input parameters is propagated through simulation by random sampling of the corresponding distribution of input parameters. Synthetic and recorded data are regressed using random-effect maximum likelihood regression algorithm by determining the compatible functional form. Sensitivity analysis is used in determining the impact of uncertainty of each input parameter on standard deviation of the regression residuals about the median prediction equation. Major contribution to total uncertainty is from Kappa factor in case of within-event terms and from stress drop in case of event-to-event variability. Predicted and recorded response spectra is matching within ±1 standard deviation for the entire period range.
Highlights New GMPE for wide range of magnitude (4–9 ) and distance (10–750 km) for the Himalayan region. Estimation of source, path and site terms for the Himalayan region using Fourier Amplitude spectrum. Calculation of confidence interval and standard error of the regression parameters using Monte-Carlo scheme. Sensitivity analysis for determining the impact of uncertainty of each input parameter on GMPE standard deviation.
Regional stochastic GMPE with available recorded data for active region – Application to the Himalayan region
Abstract New ground motion prediction equation for the active Himalayan region for a wide range of moment magnitude ( 4–9) and distance (10–750 km) is developed. For simulating the synthetic ground motions; source, path, and site terms are derived using the Fourier amplitude spectrum of the recorded ground motion data. Uncertainty of input parameters is propagated through simulation by random sampling of the corresponding distribution of input parameters. Synthetic and recorded data are regressed using random-effect maximum likelihood regression algorithm by determining the compatible functional form. Sensitivity analysis is used in determining the impact of uncertainty of each input parameter on standard deviation of the regression residuals about the median prediction equation. Major contribution to total uncertainty is from Kappa factor in case of within-event terms and from stress drop in case of event-to-event variability. Predicted and recorded response spectra is matching within ±1 standard deviation for the entire period range.
Highlights New GMPE for wide range of magnitude (4–9 ) and distance (10–750 km) for the Himalayan region. Estimation of source, path and site terms for the Himalayan region using Fourier Amplitude spectrum. Calculation of confidence interval and standard error of the regression parameters using Monte-Carlo scheme. Sensitivity analysis for determining the impact of uncertainty of each input parameter on GMPE standard deviation.
Regional stochastic GMPE with available recorded data for active region – Application to the Himalayan region
Bajaj, Ketan (author) / Anbazhagan, P. (author)
2019-08-16
Article (Journal)
Electronic Resource
English
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