Correlation model for spatially distributed ground-motion intensities

2009 ◽  
Vol 38 (15) ◽  
pp. 1687-1708 ◽  
Author(s):  
Nirmal Jayaram ◽  
Jack W. Baker
Keyword(s):  
Ground Motion ◽  
Correlation Model ◽  
Spatially Distributed

Uncertainty and Correlation for Loss Assessment of Spatially Distributed Systems

2007 ◽  
Vol 23 (4) ◽  
pp. 753-770 ◽  
Author(s):  
Renee Lee ◽  
Anne S. Kiremidjian
Keyword(s):  
Risk Assessment ◽  
Ground Motion ◽  
San Francisco ◽  
Structural Damage ◽  
Transportation Networks ◽  
Correlation Model ◽  
Loss Assessment ◽  
Monetary Loss ◽  
Coefficients Of Variation ◽  
Spatially Distributed

Seismic risk assessment for a spatially distributed system, such as a lifeline network, involves characterization of ground shaking and structural damage for multiple structures in a region. The expected value of monetary loss, a common measure of the risk, has been previously formulated but with little attention to the uncertainty around this monetary loss. Furthermore, prior research on risk assessment for lifeline systems, in particular transportation networks, assumes no spatial ground motion correlation and no structure-to-structure damage correlation between sites in the network. In this paper, a framework for treating these correlations in the network risk analysis is presented. A demonstration of this methodology is carried out for two transportation networks located in the San Francisco Bay region. Coefficients of variation for network physical loss using a non–distance dependent ground motion correlation model in the framework range between 0.6 and 1.5 for the sample networks presented here. Coefficients of variation for network physical loss using a distance-dependent ground motion correlation model in the framework range between 1.0 and 1.4 for the same networks. It is demonstrated through these applications that assuming no correlation in ground motion and in damage may potentially underestimate uncertainty in the overall loss estimation.

Seismic Hazard and Loss Analysis for Spatially Distributed Infrastructure in Christchurch, New Zealand

2016 ◽  
Vol 32 (2) ◽  
pp. 697-712 ◽  
Author(s):  
Hasan Manzour ◽  
Rachel A. Davidson ◽  
Nick Horspool ◽  
Linda K. Nozick
Keyword(s):  
New Zealand ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Regional Scale ◽  
Average Error ◽  
Loss Analysis ◽  
Spatially Distributed ◽  
Small Set ◽  
Computationally Intensive ◽  
Distributed Infrastructure

The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabilistic ground motion maps to represent the seismic hazard for analysis of spatially distributed infrastructure. We applied the method to Christchurch, New Zealand, including a sensitivity analysis of key user-specified parameters. A set of just 124 ground motion maps were able to match the hazard curves based on a million-year Monte Carlo simulation with no error at the four selected return periods, mean spatial correlation errors of 0.03, and average error in the residential loss exceedance curves of 2.1%. This enormous computational savings in the hazard has substantial implications for regional-scale, policy decisions affecting lifelines or building inventories since it can allow many more downstream analyses and/or doing them using more sophisticated, computationally intensive methods. The method is robust, offering many equally good solutions and it can be solved using free open source optimization solvers.

Macrospatial Correlation Model of Seismic Ground Motions

2005 ◽  
Vol 21 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Min Wang ◽  
Tsuyoshi Takada
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Joint Probability ◽  
Observation Data ◽  
Residual Value ◽  
Attenuation Relationship ◽  
Correlation Model ◽  
Stochastic Field ◽  
Seismic Ground Motion ◽  
Joint Probability Density

It is very important to estimate a macrospatial correlation of seismic ground motion intensities for earthquake damage predictions, building portfolio analyses etc., whereby damage in different locations has to be taken into account simultaneously. This study focuses on spatial correlation of the residual value between an observed and a predicted ground motion intensity, which is estimated by an empirical mean attenuation relationship. The residual value is modeled in such a way that the joint probability density function (PDF) of seismic ground-motion intensity can be characterized by the spatial correlation model as well as an empirical mean attenuation relationship, assuming that it constitutes a homogeneous two-dimensional stochastic field. Using the dense observation data of earthquakes that occurred in Japan and Taiwan in recent years, the macrospatial correlation model is proposed and the assumption of homogeneity is verified in this paper.

Correlations of spectral accelerations in the Chilean subduction zone

2020 ◽  
Vol 36 (2) ◽  
pp. 788-805 ◽  
Author(s):  
Gabriel Candia ◽  
Alan Poulos ◽  
Juan Carlos de la Llera ◽  
Jorge G.F. Crempien ◽  
Jorge Macedo
Keyword(s):  
Subduction Zone ◽  
Subduction Zones ◽  
Peak Ground Velocity ◽  
Data Sets ◽  
Correlation Model ◽  
Crustal Earthquakes ◽  
Correlation Models ◽  
Spatially Distributed ◽  
Soil Site ◽  
Spectral Accelerations

The correlation between spectral accelerations is key in the construction of conditional mean spectra, the computation of vector-valued seismic hazard, and the assessment of seismic risk of spatially distributed systems, among other applications. Spectral correlations are highly dependent on the earthquake database used, and thus, region-specific correlation models have been developed mainly for earthquakes in western United States, Europe, Middle East, and Japan. Correlation models based on global data sets for crustal and subduction zones have also become available, but there is no consensus about their applicability on a specific region. This study proposes a new correlation model for 5% damped spectral accelerations and peak ground velocity in the Chilean subduction zone. The correlations obtained were generally higher than those observed from shallow crustal earthquakes and subduction zones such as Japan and Taiwan. The study provides two illustrative applications of the correlation model: (1) computation of conditional spectra for a firm soil site located in Santiago, Chile and (2) computation of bivariate hazard for spectral accelerations at two structural periods.

Spatial Correlation Model of Systematic Site and Path Effects for Ground‐Motion Fields in Northern Italy

2019 ◽  
Vol 109 (4) ◽  
pp. 1419-1434 ◽  
Author(s):  
Sara Sgobba ◽  
Giovanni Lanzano ◽  
Francesca Pacor ◽  
Rodolfo Puglia ◽  
Maria D'Amico ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Sedimentary Cover ◽  
Geometric Mean ◽  
Northern Italy ◽  
Correlation Model ◽  
Loss Assessment ◽  
Period Range ◽  
Spatially Correlated ◽  
Systematic Effects

Abstract In this study, we propose an approach to generate spatially correlated seismic ground‐motion fields for loss assessment and risk analysis. Differently from the majority of spatial correlation models, usually calibrated on within‐earthquake residuals, we use the sum of the source‐, site‐, and path‐systematic effects (namely corrective terms) of the ground‐motion model (GMM), obtained relaxing the ergodic assumption. In this way, we build a scenario‐related spatial correlation model of the corrective terms by which adjusting the median predictions of ground motion and the associated variability. We show a case study focused on the Po Plain area in northern Italy, presenting a series of peculiar features (i.e., availability of a dense dataset of seismic records with uniform soil classification and very large plain with variable thickness of the sedimentary cover) that make its study particularly suitable for the purpose of developing and validating the proposed approach. The study exploits the repeatable corrective terms, estimated by Lanzano et al. (2017) in northern Italy, using a local GMM (Lanzano et al., 2016), which predicts the geometric mean of horizontal response spectral accelerations in the 0.01–4 s period range. Our results show that the implementation of a spatially correlated model of the systematic terms provides reliable shaking fields at various periods and spatial patterns compliant with the deepest geomorphology of the area, which is an aspect not accounted by the GMM model. The possibility to define a priori fields of systematic effects depending on local characteristics could be usefully adopted either to simulate future ground‐motion scenarios or to reconstruct past events.

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