A Selection of GMPEs for the United Kingdom Based on Instrumental and Macroseismic Datasets

2019 ◽  
Vol 109 (4) ◽  
pp. 1378-1400 ◽  
Author(s):  
Manuela Villani ◽  
Barbara Polidoro ◽  
Rory McCully ◽  
Thomas Ader ◽  
Ben Edwards ◽  
...  
Keyword(s):  
United Kingdom ◽  
Ground Motion ◽  
Statistical Tests ◽  
Stochastic Simulations ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Ground Acceleration ◽  
The United Kingdom ◽  
Moderate Seismicity ◽  
Selection Of

Abstract In countries with low‐to‐moderate seismicity, the selection of appropriate ground‐motion prediction equations (GMPEs) to be used in a probabilistic seismic hazard analysis (PSHA) is a challenging step. Empirical observations of ground motion are limited, and GMPEs, when available, are generally based on stochastic simulations or adjusted empirical GMPEs from elsewhere. This article investigates the suitability of recent GMPEs to the United Kingdom. To this end, the spectral accelerations obtained from available instrumental ground‐motion data in the United Kingdom with magnitude lower than 4.5 are compared with the GMPEs’ predictions through the analysis of residuals and the application of statistical tests. To compensate for the scarcity of data for the magnitude range of interest in the PSHA, a macroseismic dataset is also considered. Macroseismic intensities are converted to peak ground acceleration (PGA) and statistically compared with the PGA predicted by the GMPEs. The GMPEs are then compared in terms of median ground‐motion prediction through Sammon’s maps to evaluate their similarities. The analyses from both datasets led to six suitable GMPEs, of which three are from the Next Generation Attenuation‐West2 project, one is European, one is based mainly on a Japanese dataset, and one is a stochastic GMPE developed specifically for the United Kingdom.

scholarly journals Use of Macroseismic Intensity Data to Validate a Regionally Adjustable Ground Motion Prediction Model

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 422 ◽  
Author(s):  
Yuxiang Tang ◽  
Nelson Lam ◽  
Hing-Ho Tsang ◽  
Elisa Lumantarna
Keyword(s):  
Ground Motion ◽  
Crustal Structure ◽  
Ground Motions ◽  
Eastern China ◽  
Stochastic Simulations ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
South Eastern ◽  
Moderate Seismicity ◽  
Eastern Australia

In low-to-moderate seismicity (intraplate) regions where locally recorded strong motion data are too scare for conventional regression analysis, stochastic simulations based on seismological modelling have often been used to predict ground motions of future earthquakes. This modelling methodology has been practised in Central and Eastern North America (CENA) for decades. It is cautioned that ground motion prediction equations (GMPE) that have been developed for use in CENA might not always be suited for use in another intraplate region because of differences in the crustal structure. This paper introduces a regionally adjustable GMPE, known as the component attenuation model (CAM), by which a diversity of crustal conditions can be covered in one model. Input parameters into CAM have been configured in the same manner as a seismological model, as both types of models are based on decoupling the spectral properties of earthquake ground motions into a generic source factor and a regionally specific path factor (including anelastic and geometric attenuation factors) along with a crustal factor. Unlike seismological modelling, CAM is essentially a GMPE that can be adapted readily for use in different regions (or different areas within a region) without the need of undertaking any stochastic simulations, providing that parameters characterising the crustal structure have been identified. In addressing the challenge of validating a GMPE for use in an area where instrumental data are scarce, modified Mercalli intensity (MMI) data inferred from peak ground velocity values predicted by CAM are compared with records of MMI of past earthquake events, as reported in historical archives. South-Eastern Australia (SEA) and South-Eastern China (SEC) are the two study regions used in this article for demonstrating the viability of CAM as a ground motion prediction tool in an intraplate environment.

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 Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Greece

2020 ◽  
Author(s):  
David M. Boore ◽  
Jonathan P. Stewart ◽  
Andreas A. Skarlatoudis ◽  
Emel Seyhan ◽  
Basil Margaris ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Peak Ground Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Ground Acceleration ◽  
Magnitude Scaling ◽  
Nonlinear Site Response

ABSTRACT Using a recently completed database of uniformly processed strong-motion data recorded in Greece, we derive a ground-motion prediction model (GMPM) for horizontal-component peak ground velocity, peak ground acceleration, and 5% damped pseudoacceleration response spectra, at 105 periods ranging from 0.01 to 10 s. The equations were developed by modifying a global GMPM, to account for more rapid attenuation and weaker magnitude scaling in the Greek ground motions than in the global GMPM. Our GMPM is calibrated using the Greek data for distances up to 300 km, magnitudes from 4.0 to 7.0, and time-averaged 30 m shear-wave velocities from 150 to 1200  m/s. The GMPM has important attributes for hazard applications including magnitude scaling that extends the range of applicability to M 8.0 and nonlinear site response. These features are possible because they are well constrained by data in the global GMPM from which our model is derived. An interesting feature of the Greek data, also observed previously in studies of mid-magnitude events (6.1–6.5) in Italy, is that they are substantially overpredicted by the global GMPM, which may be a repeatable regional feature, but may also be influenced by soil–structure interaction. This bias is an important source of epistemic uncertainty that should be considered in hazard analysis.

scholarly journals Ground-motion prediction equation and site effect characterization for the central area of the Main Syncline, Upper Silesia Coal Basin, Poland

2018 ◽  
Vol 10 (1) ◽  
pp. 474-483 ◽  
Author(s):  
Maciej Jan Mendecki ◽  
Angelika Duda ◽  
Adam Idziak
Keyword(s):  
Ground Motion ◽  
Central Area ◽  
Site Effect ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Coal Basin ◽  
Ground Acceleration ◽  
Upper Silesia

Abstract The aim of the study was to find the best model of ground-motion prediction equation (GMPE) forecasting peak ground acceleration (PGA) caused by induced seismicity. The maximum values of PGA on the surface are a major seismic threat for the infrastructure, especially in the highly urbanized areas, such is the Upper Silesian Metropolitan Area. The forecasting equations were estimated based on the values of PGA, epicenter distances and mining tremor energy registered by 14 surface seismometer stations located in the central area of the Main Syncline of the Upper Silesia Coal Basin. Data were collected within the period from January 2010 to December 2016, and the total number of seismic events used in the calculations was 15 541. The final model predicted the PGA values and amplification coefficients representing the characteristics of the site effects under seismometer stations.

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