Ground-Motion Prediction Equations for Eastern North America from a Referenced Empirical Approach: Implications for Epistemic Uncertainty

2008 ◽  
Vol 98 (3) ◽  
pp. 1304-1318 ◽  
Author(s):  
G. M. Atkinson
Keyword(s):  
North America ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Empirical Approach ◽  
Motion Prediction ◽  
Eastern North America ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction

Improvement of the Quantification of Epistemic Uncertainty Using Single‐Station Ground‐Motion Prediction Equations

2019 ◽  
Vol 109 (4) ◽  
pp. 1358-1377
Author(s):  
Chih‐Hsuan Sung ◽  
Chyi‐Tyi Lee
Keyword(s):  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Strong Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Ground Acceleration ◽  
Single Station

Abstract The results of probabilistic seismic hazard analysis (PSHA) are sensitive to the standard deviation of the residuals of the ground‐motion prediction equations (GMPEs), especially for long‐return periods. Recent studies have proven that the epistemic uncertainty should be incorporated into PSHA using a logic‐tree method instead of mixing it with the aleatory variability. In this study, we propose using single‐station GMPEs with a novel approach (an epistemic‐residual diagram) to improve the quantification of epistemic uncertainty per station. The single‐station attenuation model is established from the observational recordings of a single station, hence, site‐to‐site variability (σS) can be ignored. We use 20,006 records of 497 crustal earthquakes with moment magnitudes (Mw) greater than 4.0, obtained from the Taiwan Strong Motion Instrumentation Program network, to build the single‐station GMPEs for 570 stations showing the peak ground acceleration (PGA) and spectral accelerations. A comparison is made between the total sigma of the regional GMPE (σT), the single‐station sigma of the regional GMPE as estimated by the variance decomposition method (σSS), and the sigma of single‐station GMPEs (σSS,S), for different periods. For most stations (70%), the σSS,S is about 20%–50% smaller than the σT. Furthermore, we adopt the epistemic‐residual diagram to separate the σSS,S into the epistemic uncertainty (σEP,S) and the remaining unexplained variability (σSP,S) for each station. The results show that in most areas, the σSP,S for the PGA is about 50%–80% smaller than the σT. Finally, the variations in the various sigma and model coefficients are mapped with the geographical locations of the stations for analysis of different regional characteristics.

A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

2020 ◽  
pp. 875529302095734
Author(s):  
Zach Bullock ◽  
Abbie B Liel ◽  
Shideh Dashti ◽  
Keith A. Porter
Keyword(s):  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Absolute Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Crustal Earthquakes ◽  
Cumulative Absolute Velocity ◽  
Earthquake Characteristics

Recent research has highlighted the usefulness of cumulative absolute velocity [Formula: see text] in several contexts, including using the [Formula: see text] at the ground surface for earthquake early warning and using the [Formula: see text] at rock reference conditions for evaluation of the liquefaction risk facing structures. However, there are relatively few ground motion prediction equations for CAV, they are based on relatively small data sets, and they give relatively similar results. This study develops nine ground motion prediction equations for [Formula: see text] based on a global database of ground motion records from shallow crustal earthquakes. Its provision of nine models enables characterization of epistemic uncertainty for ranges of earthquake characteristics that are sparsely populated in the regression database. The functional forms provide different perspectives on extrapolation to important ranges of earthquake characteristics, particularly large magnitude events and short distances. The variability and epistemic uncertainty in the models are characterized. Spatial autocorrelation of the models’ errors is investigated. The models’ predictions agree with existing broadly applicable models at small to moderate magnitudes and moderate to long distances. These models can be used to improve hazard analysis of [Formula: see text] that incorporates the influence of epistemic uncertainty.

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