On the Forecasting of Ground-Motion Parameters for Probabilistic Seismic Hazard Analysis

2011 ◽  
Vol 27 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Danny Arroyo ◽  
Mario Ordaz
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Prediction Models ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Motion Prediction ◽  
Probabilistic Seismic Hazard ◽  
Ground Motion Prediction ◽  
Data Set

It is well understood that the range of application for an empirical ground-motion prediction model is constrained by the range of predictor variables covered in the data used in the analysis. However, in probabilistic seismic hazard analysis (PSHA), the limits in the application of ground-motion prediction models (GMPMs) are often ignored, and the empirical relationships are extrapolated. In this paper, we show that this extrapolation leads to a quantifiable increment in the uncertainty of a GMPM when it is used to forecast a future value of a given intensity parameter. This increment, which is clearly of epistemic nature, depends on the adopted functional form, on the covariance matrix of the regression coefficients, on the used regression technique, and on the quality of the data set. In addition, through some examples using the database of the Next Generation of Ground-Motion Attenuation Models project and some currently favored functional forms we study the increment in the seismic hazard produced by the extrapolation of GMPMs.

Ranking of Ground-Motion Models (GMMs) for Use in Probabilistic Seismic Hazard Analysis for Iran Based on an Independent Data Set

2020 ◽  
Author(s):  
Zoya Farajpour ◽  
Milad Kowsari ◽  
Shahram Pezeshk ◽  
Benedikt Halldorsson
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Selection Process ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2  s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

A ground motion based procedure to identify the earthquakes that are the most relevant for probabilistic seismic hazard analysis

2021 ◽  
pp. 875529302098198
Author(s):  
John G Anderson ◽  
Fabrice Cotton ◽  
Dino Bindi
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Time Window ◽  
Hazard Analysis ◽  
Prediction Equation ◽  
Seismic Hazard Analysis ◽  
Motion Prediction ◽  
Probabilistic Seismic Hazard ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction

A method is proposed to identify within seismic catalogs those earthquakes that are most relevant to the seismic hazard. The approach contrasts with the classical approach to decluster the seismic catalog with the expectation that the remaining main shocks will be the relevant events for the seismic hazard analysis. We apply a time window like in the window declustering approach of Gardner and Knopoff, but the time window is motivated by relevance to engineering. A ground motion criterion replaces the spatial window. An event in the time window is included in the “Maximum Shaking Earthquake Catalog (MSEQ catalog)” if the median ground motion at its epicenter exceeds the predicted median ground motion there from the main shock, using a locally appropriate ground motion prediction equation. Ground motion can be measured by any parameter that is estimated by a ground motion prediction equation. We consider peak acceleration and spectral amplitude (SA) at periods of 0.2, 1.0, and 3.0 s. The longer period parameters systematically remove more small events. The purpose is not to produce a declustered catalog, in which each group of physically related earthquakes is represented by its largest event. Statistical properties of the MSEQ catalog somewhat resemble the corresponding declustered catalog in three tested regions, but the MSEQ catalogs all retain more large-magnitude earthquakes. The MSEQ catalog may better represent the potential hazard in a region, and thus might be considered as an alternative to a declustered catalog in developing the seismicity model for probabilistic seismic hazard analysis.

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