Ground‐Motion Prediction Equations for Region‐Specific Probabilistic Seismic‐Hazard Analysis

Lanzano, Giovanni; D’Amico, Maria; Felicetta, Chiara; Puglia, Rodolfo; Luzi, Lucia; Pacor, Francesca; Bindi, Dino

doi:10.1785/0120150096

Cited by 37 publications

(24 citation statements)

References 38 publications

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“…To increase the number of recordings for magnitude between 5 and 6, the calibration data set includes 655 recordings relevant to the 2009 L'Aquila sequence and 233 recordings for the 2012 Emilia sequence (Figures b and c). While we assume that the source scaling in equation is the same for the two sequences (i.e., parameters A and B ), previous studies highlighted peculiarities in the attenuation with distance for the recordings of the Emilia sequence [ Lanzano et al , ]. Therefore, equation is modified by introducing two different sets of attenuation coefficients:

C_{j}^{NI}

(where NI stands for Northern Italy) and

C_{j}^{CI}

(where CI stands for Central Italy) for Emilia and L'Aquila recordings, respectively.…”

Section: Iv2 Versus Radiated Energymentioning

confidence: 99%

Rapid determination of P wave‐based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence

Picozzi

Bindi

Brondi

et al. 2017

Geophysical Research Letters

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We propose a P wave based procedure for the rapid estimation of the radiated seismic energy, and a novel relationship for obtaining an energy‐based local magnitude (MLe) measure of the earthquake size. We apply the new procedure to the seismic sequence that struck Central Italy in 2016. Scaling relationships involving seismic moment and radiated energy are discussed for the Mw 6.0 Amatrice, Mw 5.9 Ussita, and Mw 6.5 Norcia earthquakes, including 35 ML > 4 aftershocks. The Mw 6.0 Amatrice earthquake shows the highest apparent stress, and the observed differences among the three main events highlight the dynamic heterogeneity with which large earthquakes can occur in Central Italy. Differences between estimates of MLe and Mw allows identification of events which are characterized by a higher proportion of energy being transferred to seismic waves, providing important real‐time indications of earthquakes shaking potential.

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C_{j}^{NI}

(where NI stands for Northern Italy) and

C_{j}^{CI}

(where CI stands for Central Italy) for Emilia and L'Aquila recordings, respectively.…”

Section: Iv2 Versus Radiated Energymentioning

confidence: 99%

Rapid determination of P wave‐based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence

Picozzi

Bindi

Brondi

et al. 2017

Geophysical Research Letters

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“…The GMMs discussed above have been applied in several recent PSHA exercises; however, these GMMs have been developed without consideration of the spatial correlation in ground motions, possibly due to the complexity in the estimation process. For instance, Jayaram and Baker developed spatial correlation models for several global earthquakes using the NGA‐West (Next Generation of Ground‐Motion Attenuation Models for the Western US) GMMs (eg, CY08), proposing a predictive model of the effective range parameter

\overset{h}{true}

(ie, the separation distance between stations at which the spatial correlation is about 0.05) as a function of the structural period T (in seconds).…”

Section: Introduction and Motivationsmentioning

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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“…The between-event standard deviation τ of SI17ref is significantly lower than for ITA10, with a reduction in the range of 40% to 50%. This is expected, as a local dataset is used for the model calibration (see also [64]), and the event metadata in ESM were revised and improved with respect to those used for ITA10. Finally, the within-event variability of SI17ref is larger than for ITA10.…”

Section: Empirical Gmpesmentioning

confidence: 99%

Hybrid GMPEs for Region-Specific PSHA in Southern Italy

et al. 2018

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This paper describes the main findings of the project HYPSTHER (HYbrid ground motion prediction equations for PSha purposes: the study case of souTHERn Italy; supported by the Italian Institute of Geophysics and Volcanology). The goal of the project is to develop a methodological approach to retrieve hybrid Ground Motion Prediction Equations (GMPEs) based on integration of recorded and synthetic data. This methodology was applied to the study area of southern Italy, focusing on the southern Calabria and Sicily regions. The target area was chosen due to the expected high seismic hazard levels, despite the low seismic activity in recent decades. In addition, along the coast of the study area, there are many critical infrastructures, such as chemical plants, refineries, and large ports, which strongly increase the risk of technological accidents induced by earthquakes. Through the synthetic data, the predictions of the hybrid GMPEs have been improved under near-field conditions, with respect to empirical models for moderate to large earthquakes. Attenuation at distances greater than 50 km is instead controlled by the empirical data, because attenuation is faster with distance. The aleatory variability of the hybrid models has strong impact on probabilistic seismic hazard assessment, as it is lower than the sigma of the empirical GMPEs. The use of the hybrid GMPEs specific for the study area can produce remarkable reductions in hazard levels for long-return periods, mainly due to changes in median predictions and reduction of the aleatory variability.

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Ground‐Motion Prediction Equations for Region‐Specific Probabilistic Seismic‐Hazard Analysis

Cited by 37 publications

References 38 publications

Rapid determination of P wave‐based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence

Rapid determination of P wave‐based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence

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

Hybrid GMPEs for Region-Specific PSHA in Southern Italy

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