Multivariate Bayesian Regression Analysis Applied to Ground-Motion Prediction Equations, Part 2: Numerical Example with Actual Data

Arroyo, Danny; Ordaz, Mario

doi:10.1785/0120090320

Cited by 25 publications

(16 citation statements)

References 21 publications

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“…Although the current practice estimates the GMM for each IM individually and then assess the cross‐IM correlation coefficients/models separately, several studies have shown that it is possible but challenging to incorporate the spatial cross‐IM (ie, considering both spatial correlation and cross‐IM correlation at the same time) in the GMM estimation process, requiring often stricter assumptions. The developed one‐stage estimation approach can also account for the cross‐IM correlation, and this feature is currently under investigation by the authors, especially in terms of implications on the GMM estimates.…”

Section: Methodsmentioning

confidence: 99%

Ground‐motion intensity measure correlations observed in Italian strong‐motion records

Huang

Galasso

2019

Earthq Engng Struct Dyn

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Summary Ground‐motion models (GMMs) are widely used in probabilistic seismic hazard analysis (PSHA) to estimate the probability distributions of earthquake‐induced ground‐motion intensity measures (IMs) at a site, given an earthquake of a certain magnitude occurring at a nearby location. Accounting for spatial and cross‐IM correlations in earthquake‐induced ground motions has important implications on probabilistic seismic hazard and loss estimates. This study first develops a new Italian GMM with spatial correlation for 31 amplitude‐related IMs, including peak ground acceleration (PGA), peak ground velocity (PGV), and 5%‐damped elastic pseudo‐spectral accelerations (PSAs) at 29 periods ranging from 0.01 to 4 seconds. The model estimation is performed through a recently developed one‐stage nonlinear regression algorithm proposed by the authors, known as the Scoring estimation approach. In fact, current state‐of‐practice approaches estimate spatial correlation separately from the GMM estimation, resulting in inconsistent and statistically inefficient estimators of interevent and intraevent variances and parameters in the spatial correlation model. We test whether this affects the subsequent cross‐IM correlation analysis. To this aim, based on the newly developed GMM, the empirical correlation coefficients from interevent and intraevent residuals are investigated. Finally, a set of analytical correlation models between the selected IMs are proposed. This is of special interest as several correlation models between different IMs have been calibrated and validated based on advanced GMMs and global datasets, lacking earthquakes in extensional regions; however, modeling the correlation between different IM types has not been adequately addressed by current, state‐of‐the‐art GMMs and recent ground‐motion records for Italy.

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Section: Methodsmentioning

confidence: 99%

Ground‐motion intensity measure correlations observed in Italian strong‐motion records

Huang

Galasso

2019

Earthq Engng Struct Dyn

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“…We have not included a sound discussion because a synthetic example is presented. In a companion article (Arroyo and Ordaz, 2010) we use a set of actual ground-motion records and we present a complete discussion on how the prior information can be defined.…”

Section: Prior Information For the Bayesian Methodsmentioning

confidence: 99%

Multivariate Bayesian Regression Analysis Applied to Ground-Motion Prediction Equations, Part 1: Theory and Synthetic Example

Arroyo

Ordaz²

2010

Bulletin of the Seismological Society of America

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An application of a linear multivariate Bayesian regression model to compute pseudoacceleration (SA) ground-motion prediction equations (GMPEs) is presented. The model is able to include the correlation between observations for a given earthquake, the correlation between SA ordinates at different periods, and the correlation between regression coefficients of the ground-motion prediction model. We evaluate the advantages of the Bayesian approach over the traditional regression methods, and we discuss the differences between univariate and multivariate analyses. Because the application of the Bayesian method is in general complex and implies an increase in the numerical effort with respect to the traditional methods, our computer code to perform linear Bayesian analyses is freely available on request.

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“…Earthquakes from this catalog were selected from January 1 st , 1976 to December 31, 2014, within the geographic coordinates 15ºN ≥ latitude ≤ 24ºN latitudes and 106ºW ≤ longitude ≥ 92º W. All the events have magnitudes Mw ≥ 4.7 and focal depths of less than 40 km. In this case, the GMPE's estimated by Arroyo and Ordaz, (2010). for subduction zone earthquakes were utilized.…”

Section: Attenuation Modelsmentioning

confidence: 99%

A probabilistic seismic hazard assessment of the Trans-Mexican Volcanic Belt, Mexico based on historical and instrumentally recorded seismicity

Bayona

Reynoso

Schroeder

2017

GeofInt

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The Trans-Mexican Volcanic Belt (TMVB) is an active volcanic chain being deformed by an intra-arc extensional fault network. Although several crustal earthquakes with magnitude>7 have originated in the TMVB since the 16th century, the background seismicity of this geological structure is very low and the region is usually considered of low seismic hazard. In this study, we present an updated probabilistic seismic hazard model of the TMVB. The seismicity catalog used here includes forty-three historically and instrumentally recorded earthquakes, from 1858 to 2014; five of these are large earthquakes that occurred in the TMVB during the XIXth century. Due to the lack of a statically representative sample, we propose, in a qualitative manner, the seismicity catalog is complete for Mâ‰¥4 since 1964 and for Mâ‰¥6 since 1858. Moreover, we introduce three different earthquake frequency-magnitude relations. The first one is a conventional Gutenberg Richter fit of the distribution of the instrumentally recorded earthquakes data. The other two are non-conventional, semi-parametric approaches that integrate the historical and the instrumental data to determine seismicity rates in the region. Our preferred model (seismicity model B) fits separately the instrumental and the historical data and merge the two fits into one curve. A uniform seismic hazard (USH) of the TMVB for a return period of 500 years was calculated considering three major sources of earthquakes: 1) Subduction thrust-faulting events in the Middle American Trench (MAT); 2) Earthquakes within the subducted Cocos plate and, 3) Shallow crustal earthquakes in the TMVB. According to the seismicity model B, the average recurrence time of a Mâ‰¥7 earthqua-ke on the TMVB is approximately 150 years. In contrast, the recurrence time estimated from the instrumental catalog is 12,000 years. The results of this seismicity model, which is based on historical and instrumental data, agrees also with the return periods of prehistoric earthquakes, estimated for short segments of the fault system in the TMVB in paleoseismological studies. When comparing the results of our preferred seismicity model, the PGA estimated using only the instrumental seismicity are 18 to 56% smaller than those predicted by the model using the historical catalog.

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Multivariate Bayesian Regression Analysis Applied to Ground-Motion Prediction Equations, Part 2: Numerical Example with Actual Data

Cited by 25 publications

References 21 publications

Ground‐motion intensity measure correlations observed in Italian strong‐motion records

Ground‐motion intensity measure correlations observed in Italian strong‐motion records

Multivariate Bayesian Regression Analysis Applied to Ground-Motion Prediction Equations, Part 1: Theory and Synthetic Example

A probabilistic seismic hazard assessment of the Trans-Mexican Volcanic Belt, Mexico based on historical and instrumentally recorded seismicity

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