A Novel Spatial Correlation Model Based on Anisotropy of Earthquake Ground‐Motion Intensity

2017 ◽  
Vol 107 (6) ◽  
pp. 2809-2820 ◽  
Author(s):  
Alireza Garakaninezhad ◽  
Morteza Bastami
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Earthquake Ground Motion ◽  
Correlation Model ◽  
Model Based

Spatial Correlation Interpretation of Effects of As+ Implantation on the Raman Spectra of GaAs

1983 ◽  
Vol 27 ◽  
Author(s):  
D.E. Aspnes ◽  
K.K. Tiong ◽  
P.M. Amirtharaj ◽  
F.H. Pollak
Keyword(s):  
Spatial Correlation ◽  
Raman Spectra ◽  
Selection Rule ◽  
Phonon Mode ◽  
Red Shift ◽  
Correlation Model ◽  
Model Based ◽  
Ion Implanted

ABSTRACTThe red shift and asymmetric broadening of the LO phonon mode of ion-implanted GaAs are both described quantitatively by a spatial correlation model based on a damage-induced relaxation of the momentum selection rule previously used by Richter, Wang, and Ley to describe similar effects in microcrystalline Si. The success of the model for a qualitatively different disorder microstructure suggests it may be possible to evaluate average sizes of crystallographically perfect regions in semiconductors from the phonon lineshapes of their Raman spectra.

A New Spatial Correlation Model Based on the Distributed RC-∏ Model

2014 ◽  
Vol 989-994 ◽  
pp. 2204-2207
Author(s):  
Xiao Xiao Liu ◽  
Jing Bo Shao ◽  
Ling Ling Zhao
Keyword(s):  
Gaussian Process ◽  
Spatial Correlation ◽  
Process Variations ◽  
Deep Submicron ◽  
Correlation Model ◽  
Crosstalk Noise ◽  
Model Based ◽  
Reduction Techniques ◽  
Non Gaussian ◽  
Tree Branch

To solve the crosstalk noise question in deep-submicron technologies, a new spatial correlation model based on the distributed RC-π model is proposed in this paper. Quiet aggressor net and tree branch reduction techniques are introduced to the distributed RC-π model, and a new spatial correlation model of both Gaussian and non-Gaussian process variations among segments is created. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.

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.

scholarly journals Spatial correlation of broadband earthquake ground motion in Norcia (Central Italy) from physics-based simulations

2021 ◽  
Author(s):  
Erika Schiappapietra ◽  
Chiara Smerzini
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Near Field ◽  
Geometric Mean ◽  
Central Italy ◽  
Earthquake Ground Motion ◽  
Physical Parameters ◽  
Spatial Correlations ◽  
Ground Motion Scenarios ◽  
The Impact

AbstractThis paper investigates the spatial correlation of response spectral accelerations from a set of broadband physics-based ground motion simulations generated for the Norcia (Central Italy) area by means of the SPEED software. We produce several ground-motion scenarios by varying either the slip distribution or the hypocentral location as well as the magnitude to systematically explore the impact of such physical parameters on spatial correlations. We extend our analysis to other ground-motion components (vertical, fault-parallel, fault-normal) in addition to the more classic geometric mean to highlight possible ground-motion directionality and therefore identify specific spatial correlation features. Our analyses provide useful insights on the role of slip heterogeneities as well as the relative position between hypocentre and slip asperities on the spatial correlation. Indeed, we found a significant variability in terms of both range and sill among the considered case studies, suggesting that the spatial correlation is not only period-dependent, but also scenario-dependent. Finally, our results reveal that the isotropy assumption may represent an oversimplification especially in the near-field and thus it may be unsuitable for assessing the seismic risk of spatially-distributed infrastructures and portfolios of buildings.

scholarly journals Assessment of the uncertainty in spatial-correlation models for earthquake ground motion due to station layout and derivation method

2021 ◽  
Author(s):  
Erika Schiappapietra ◽  
John Douglas
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Spatial Correlation ◽  
Ground Motion ◽  
Weighted Least Squares ◽  
Correlation Structure ◽  
Estimation Methods ◽  
Lower Percentage ◽  
Earthquake Ground Motion ◽  
Spatial Correlation Structure

AbstractThe evaluation of the aggregate risks to spatially distributed infrastructures and portfolios of buildings requires quantification of the estimated shaking over a region. To characterize the spatial dependency of ground motion intensity measures (e.g. peak ground acceleration), a common geostatistical tool is the semivariogram. Over the past decades, different fitting approaches have been proposed in the geostatistics literature to fit semivariograms and thus characterize the correlation structure. A theoretically optimal approach has not yet been identified, as it depends on the number of observations and configuration layout. In this article, we investigate estimation methods based on the likelihood function, which, in contrast to classical least-squares methods, straightforwardly define the correlation without needing further steps, such as computing the experimental semivariogram. Our outcomes suggest that maximum-likelihood based approaches may outperform least-squares methods. Indeed, the former provides correlation estimates, that do not depend on the bin size, unlike ordinary and weighted least-squares regressions. In addition, maximum-likelihood methods lead to lower percentage errors and dispersion, independently of both the number of stations and their layout as well as of the underlying spatial correlation structure. Finally, we propose some guidelines to account for spatial correlation uncertainty within seismic hazard and risk assessments. The consideration of such dispersion in regional assessments could lead to more realistic estimations of both the ground motion and corresponding losses.

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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