scholarly journals Lessons Learned from Applying Varying Coefficient Model to Controlled Simulation Datasets

Author(s):  
Xiaofeng Meng ◽  
Christine Goulet

Abstract The development of site- and path-specific (i.e., non-ergodic) ground motion models (GMMs) can drastically improve the accuracy of probabilistic seismic hazard analyses (PSHA). The Varying Coefficient Model (VCM) is a novel technique for developing non-ergodic GMMs, which puts epistemic uncertainty into spatially varying coefficients. The coefficients at nearby locations are correlated by placing a Gaussian process prior on them. The correlation structure is determined by the data, and later used to predict coefficients and their epistemic uncertainties at new locations. It is important to carefully verify the technique before its results can be accepted by the engineering community. In this study, we used a series of simulation-based controlled ground motion datasets from CyberShake to test a modified VCM technique, which partitions the epistemic uncertainty into spatially varying source, site and path terms. Because the simulation parameters (inputs) are known, it is straightforward to verify what is recovered by the VCM from CyberShake simulation. We find that the site effects in CyberShake datasets can be reliably recovered by the VCM. However, the densely-located self-similar events in CyberShake datasets lead to large correlation lengths, which violates the isotropic assumption underlying the method and prevents the VCM from capturing the genuine source effects. For path effects, cell-specific attenuation approaches fail to recover the anelastic attenuation pattern of the 3D velocity model, most likely due to inappropriate assumption of point sources and straight-line wave propagation. Instead, a midpoint approach that only considers the aggregated path effects can better recover the strong attenuation within basins by fixing the correlation length of path effects. Lessons learned in this study not only provide important guidance for future applications of VCM to both simulation and empirical datasets, but also help further development of the technique, notably for the recovery of path effects.

Author(s):  
Giovanni Lanzano ◽  
Sara Sgobba ◽  
Luca Caramenti ◽  
Alessandra Menafoglio

ABSTRACT In this article, we implement a new approach to calibrate ground-motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS-GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground-motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%-damped acceleration response spectra in the period interval T=0.01–10  s. The resulting MS-GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%–20%. The article also provides scenario-dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade-off inherent to site and path effects, which limits their physical interpretation.


2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson ◽  
Nicolas M. Kuehn

Abstract A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to 40% smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past event.


2021 ◽  
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
John P Wilson ◽  
Huangyuan Tan ◽  
Tianyou Chu

Abstract Background: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of health risk factors on COVID-19 rates in different places, but the problem of spatial heterogeneity has not been adequately addressed.Methods: In this paper, we developed an Eigenvector Spatial Filtering based spatially varying coefficient model (ESF-SVC) to reveal the spatially varying impact of certain health risk factors on the COVID-19 spread. The experiment was conducted during 7 weeks within two study extents (Hubei province and mainland China). Spatial varying coefficient maps were produced for spatial pattern discovery.Results: Results showed that the ESF-SVC model could take good control of over-fitting problems, with average adjusted R2 16.31% (in Hubei province) and 10.25% (in mainland China) higher than that of GWR. The cross validation RMSE of ESF-SVC model was also the lowest. In Hubei province, Population density and wind speed had a significant impact on COVID-19 infection rates and that their effect was constant across cities. While in mainland China, migration score, building density, temperature and altitude showed significant impact and their effect varies across space. The influence of migration score was less contributive and less significant in cities around Wuhan than cities farther away, while the altitude showed stronger contributions in high altitude cities.Conclusions: Our study hopes to provide not only a feasible path to solve the problem of spatial autocorrelation and spatial heterogeneity in COVID-19 characterization but also an intuitive way to discover spatial patterns in large study areas, which could help people and government awareness of the potential health risks and shed some light in COVID-19 control strategies.


2014 ◽  
Vol 8 ◽  
pp. 23-33 ◽  
Author(s):  
Matthew J. Heaton ◽  
Stephan R. Sain ◽  
Tamara A. Greasby ◽  
Christopher K. Uejio ◽  
Mary H. Hayden ◽  
...  

2016 ◽  
Vol 106 (6) ◽  
pp. 2574-2583 ◽  
Author(s):  
Niels Landwehr ◽  
Nicolas M. Kuehn ◽  
Tobias Scheffer ◽  
Norman Abrahamson

2021 ◽  
Author(s):  
Claudia Abril ◽  
Martin Mai ◽  
Benedikt Halldórsson ◽  
Bo Li ◽  
Alice Gabriel ◽  
...  

<p>The Tjörnes Fracture Zone (TFZ) in North Iceland is the largest and most complex zone of transform faulting in Iceland, formed due to a ridge-jump between two spreading centers of the Mid-Atlantic Ridge, the Northern Volcanic Zone and Kolbeinsey Ridge in North Iceland. Strong earthquakes (Ms>6) have repeatedly occurred in the TFZ and affected the North Icelandic population. In particular the large historical earthquakes of 1755 (Ms 7.0) and 1872 (doublet, Ms 6.5), have been associated with the Húsavı́k-Flatey Fault (HFF), which is the largest linear strike-slip transform fault in the TFZ, and in Iceland. We simulate fault rupture on the HFF and the corresponding near-fault ground motion for several potential earthquake scenarios, including scenario events that replicate the large 1755 and 1872 events. Such simulations are relevant for the town of Húsavı́k in particular, as it is located on top of the HFF and is therefore subject to the highest seismic hazard in the country. Due to the mostly offshore location of the HFF, its precise geometry has only recently been studied in more detail. We compile updated seismological and geophysical information in the area, such as a recently derived three-dimensional velocity model for P and S waves. Seismicity relocations using this velocity model, together with bathymetric and geodetic data, provide detailed information to constrain the fault geometry. In addition, we use this 3D velocity model to simulate seismic wave propagation. For this purpose, we generate a variety of kinematic earthquake-rupture scenarios, and apply a 3D finite-difference method (SORD) to propagate the radiated seismic waves through Earth structure. Slip distributions for the different scenarios are computed using a von Karman autocorrelation function whose parameters are calibrated with slip distributions available for a few recent Icelandic earthquakes. Simulated scenarios provide synthetic ground motion and time histories and estimates of peak ground motion parameters (PGA and PGV) at low frequencies (<2 Hz) for Húsavík and other main towns in North Iceland along with maps of ground shaking for the entire region [130 km x 110 km]. Ground motion estimates are compared with those provided by empirical ground motion models calibrated to Icelandic earthquakes and dynamic fault-rupture simulations for the HFF. Directivity effects towards or away from the coastal areas are analyzed to estimate the expected range of shaking. Thick sedimentary deposits (up to ∼4 km thick) located offshore on top of the HFF (reported by seismic, gravity anomaly and tomographic studies) may affect the effective depth of the fault's top boundary and the surface rupture potential. The results of this study showcase the extent of expected ground motions from significant and likely earthquake scenarios on the HFF. Finite fault earthquake simulations complement the currently available information on seismic hazard for North Iceland, and are a first step towards a systematic and large-scale earthquake scenario database on the HFF, and for the entire fault system of the TFZ, that will enable comprehensive and physics-based hazard assessment in the region.</p>


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