Bayesian inference of a physical seismological model for earthquake strong-motion in south Iceland

2020 ◽  
Vol 138 ◽  
pp. 106219
Author(s):  
Tim Sonnemann ◽  
Benedikt Halldorsson ◽  
Birgir Hrafnkelsson ◽  
Sigurjón Jónsson
Keyword(s):  
Bayesian Inference ◽  
Strong Motion ◽  
Seismological Model

scholarly journals A methodological approach to update Ground Motion Prediction Models using Bayesian Inference

2021 ◽  
Author(s):  
Saran Srikanth Bo ◽  
Merlin Keller ◽  
Abhinav Gupta ◽  
Gloria Senfaute
Keyword(s):  
Bayesian Inference ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Methodological Approach ◽  
Strong Motion ◽  
Motion Prediction ◽  
Data Sets ◽  
Ground Motion Prediction ◽  
Small Magnitude ◽  
Moderate Seismicity

Abstract In recent decades, prediction of ground motion at a specific site or a region is of primary interest in probabilistic seismic hazard assessment (PSHA). Historically, several ground motion prediction equation (GMPE) models with different functional forms have been published using strong ground motion records available from NGA-West and European databases. However, low-to-moderate seismicity regions, such as Central & Eastern United States and western Europe, is characterized by limited strong-motion records in the magnitude-distance range of interest for PSHA. In these regions, the available data for the development of empirical GMPEs is very scarce and limited to small magnitude events. For these regions, the general practice in PSHA is to consider a set of GMPEs developed from data sets collected in other regions with high seismicity. This practice generates an overestimation of the seismic hazard for the low seismicity regions. There are two potential solutions to overcome this problem: (i) a new GMPE model can be developed; however, development of such a model can require significant amount of data which is not usually available, and (ii) the existing GMPE models can be recalibrated based on the data sets collected in the new region rather than developing a new GMPE model. In this paper, we propose a methodological approach to recalibrate the coefficients in a GMPE model using different algorithms to perform Bayesian inference. The coefficients are recalibrated for a subset of European Strong-Motion (ESM) database that corresponds to low-to-moderate seismicity records. In this study, different statistical models are compared based on the functional form given by the chosen GMPE, and the best model and algorithm are recommended using the concept of information criteria.

A Computational Tool for Ground-Motion Simulations Incorporating Regional Crustal Conditions

2020 ◽  
Author(s):  
Yuxiang Tang ◽  
Nelson Lam ◽  
Hing-Ho Tsang
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Prediction Equation ◽  
Stochastic Simulations ◽  
Computational Tool ◽  
Conditional Mean Spectrum ◽  
Real Earthquake ◽  
Strong Motion Database ◽  
User Friendly ◽  
Seismological Model

Abstract This article introduces a computational tool, namely ground-motion simulation system (GMSS), for generating synthetic accelerograms based on stochastic simulations. The distinctive feature of GMSS is that it has two independently developed upper-crustal models (expressed in the form of shear-wave velocity profiles), which have been built into the program for deriving the frequency-dependent crustal factors, and one of these models was originally developed by the authors. GMSS also has provisions to allow the user to specify their own preferred crustal profile. Sufficient details of both crustal models (forming part of the seismological model) and the accelerogram simulation methodology are presented herein in one article, to allow any person who has programming skills (on a user-friendly platform such as MATLAB, see Data and Resources), to develop their computational tools to implement any further innovations in crustal modeling for direct engineering applications. Crustal properties deep into bedrock can only be accounted for implicitly by conventional ground-motion prediction equation (GMPE) as much depends on the region where the ground motion was recorded. This limitation of existing GMPEs poses a challenge to engineering in regions that are not well represented by any strong-motion database. Toward the end of this article, readers are enlightened with the potential transdisciplinary utility of using GMSS, to facilitate the retrieval and scaling of accelerograms sourced from a database of real earthquake records through the construction of a conditional mean spectrum.

THE EMPIRICAL GREEN’S FUNCTION TECHNIQUE MODIFICATION FOR ACCELEROGRAM SYNTHESIS IN LOWSEISMICITY REGIONS

2018 ◽  
Vol 12 (5-6) ◽  
pp. 72-80
Author(s):  
A. A. Krylov
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Main Shock ◽  
Amplitude Spectrum ◽  
Strong Motion ◽  
Function Technique ◽  
Focal Region ◽  
Empirical Green’S Function ◽  
Epicentral Zone ◽  
Empirical Green's Function

In the absence of strong motion records at the future construction sites, different theoretical and semi-empirical approaches are used to estimate the initial seismic vibrations of the soil. If there are records of weak earthquakes on the site and the parameters of the fault that generates the calculated earthquake are known, then the empirical Green’s function can be used. Initially, the empirical Green’s function method in the formulation of Irikura was applied for main shock record modelling using its aftershocks under the following conditions: the magnitude of the weak event is only 1–2 units smaller than the magnitude of the main shock; the focus of the weak event is localized in the focal region of a strong event, hearth, and it should be the same for both events. However, short-termed local instrumental seismological investigation, especially on seafloor, results usually with weak microearthquakes recordings. The magnitude of the observed micro-earthquakes is much lower than of the modeling event (more than 2). To test whether the method of the empirical Green’s function can be applied under these conditions, the accelerograms of the main shock of the earthquake in L'Aquila (6.04.09) with a magnitude Mw = 6.3 were modelled. The microearthquake with ML = 3,3 (21.05.2011) and unknown origin mechanism located in mainshock’s epicentral zone was used as the empirical Green’s function. It was concluded that the empirical Green’s function is to be preprocessed. The complex Fourier spectrum smoothing by moving average was suggested. After the smoothing the inverses Fourier transform results with new Green’s function. Thus, not only the amplitude spectrum is smoothed out, but also the phase spectrum. After such preliminary processing, the spectra of the calculated accelerograms and recorded correspond to each other much better. The modelling demonstrate good results within frequency range 0,1–10 Hz, considered usually for engineering seismological studies.

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

How do academics assess the results of multiple experiments?

2018 ◽  
Author(s):  
Olmo Van den Akker ◽  
Linda Dominguez Alvarez ◽  
Marjan Bakker ◽  
Jelte M. Wicherts ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Bayesian Inference ◽  
Secondary Analysis ◽  
Individual Participant Data ◽  
Additional Experiment ◽  
Underlying Theory ◽  
Individual Participant ◽  
Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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