scholarly journals Evaluation of Earthquake Ground Motion Together with Probability of Occurrence Based on Seismic Hazard Analysis

2011 ◽  
Vol 52 (1) ◽  
pp. 39-44
Author(s):  
Kimitoshi SAKAI ◽  
Yoshitaka MURONO
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Earthquake Ground Motion ◽  
Probability Of Occurrence

scholarly journals PSHRisk-Tool: A Python-Based Computational Tool for Developing Site Seismic Hazard Analysis and Failure Risk Assessment of Infrastructure

2020 ◽  
Vol 10 (21) ◽  
pp. 7487
Author(s):  
Tahmina Tasnim Nahar ◽  
Md Motiur Rahman ◽  
Dookie Kim
Keyword(s):  
Risk Assessment ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Earthquake Ground Motion ◽  
Earthquake Hazards ◽  
Failure Risk ◽  
Test Models ◽  
Almost All

To quantify the annual probability of earthquake ground motion (GM) exceeding a given threshold, the extensively used method named by probabilistic seismic hazard analysis (PSHA) can be adopted. The PSHA software made this method more effortless for estimating earthquake hazards for a seismic site. The main motivation of the PSHRisk-tool is to evaluate the PSHA by a user-friendly graphical interface as well as identify the intensities of GM, which will contribute to the most vulnerable condition for the infrastructure. This python-code based tool can demonstrate the source identification, probability distribution plot of magnitude and distance, formulate the hazard curve according to almost all ground motion prediction equations (GMPEs). The deaggregation for each intensity measure (IM) and the effect of seismic parameters in each GMPE can also be determined. Alongside this, the combination of the failure frequency and the hazard analysis for identifying risk assessment separates this tool from the other existing PSHA software. Accurate verification with analytical and existing test models and a case study inspires its acceptance rate. However, with the quickest and easiest way users can determine the seismic hazard analysis for any location. Failure risk analysis can be evaluated simply based on the structural failure parameters.

scholarly journals A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe: regionalisation, model selection and development of a scaled backbone approach

2020 ◽  
Vol 18 (14) ◽  
pp. 6119-6148
Author(s):  
Graeme Weatherill ◽  
Fabrice Cotton
Keyword(s):  
United States ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Eastern United States ◽  
Logic Tree ◽  
Short Period ◽  
Northeastern Europe

Abstract Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.

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.

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