Construction of a Ground-Motion Logic Tree through Host-to-Target Region Adjustments Applied to an Adaptable Ground-Motion Prediction Model

The purpose of a median ground-motion logic tree is to capture the center, body, and range of possible ground-motion amplitudes for each earthquake scenario considered in a seismic hazard analysis. For site-specific hazard analyses, the traditional approach of populating the logic tree branches with ground-motion prediction models (GMPMs) selected and weighted on the basis of vaguely defined applicability to the target region is rapidly being abandoned in favor of the backbone GMPM approach. In this approach, the selected backbone model is first adjusted to match the earthquake source and path characteristics of the target region, and then it is separately adjusted to account for the site-specific geotechnical profile. For a GMPM to be amenable to such host-to-target adjustments, the magnitude scaling of response spectral ordinates should be consistent with the theoretical scaling of Fourier amplitude spectra. In addition, the influence of individual source and path parameters should be clearly distinguished in the model to allow the adjustments to be applied individually, and reliable estimates of the source and path parameters from the host region of the GMPM should be available, as should a reference rock profile for the model. The NGA-West2 project GMPM of Chiou and Youngs (2014; hereafter, CY14) has been identified as a very suitable backbone model. Moreover, rather than adopting generic source and path parameters and a rock site profile from the host region for CY14, which is not easily defined because the data from which it was derived came from several geographical locations, recent studies have inverted the model to obtain a CY14-consistent reference rock profile and CY14-compatible source and path parameters. Using these host-region characteristics, this study illustrates the process of building a ground-motion logic tree through the sequential application of multiple host-to-target-region adjustments, each represented by a node on the logic tree to achieve a tractable model for the total epistemic uncertainty.

[1]  J. Bommer,et al.  Host-region parameters for an adjustable model for crustal earthquakes to facilitate the implementation of the backbone approach to building ground-motion logic trees in probabilistic seismic hazard analysis , 2022, Earthquake Spectra.

[2]  N. Abrahamson,et al.  A Methodology for the Development of 1D Reference VS Profiles Compatible with Ground-Motion Prediction Equations: Application to NGA-West2 GMPEs , 2021 .

[3]  D. Boore,et al.  A Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Greece , 2020, Bulletin of the Seismological Society of America.

[4]  Julian J. Bommer,et al.  Selecting Ground-Motion Models for Site-Specific PSHA: Adaptability versus Applicability , 2020, Bulletin of the Seismological Society of America.

[5]  Julian J. Bommer,et al.  Capturing epistemic uncertainty in site response , 2020 .

[6]  N. Abrahamson,et al.  Central and Eastern North America Ground-Motion Characterization (NGA-East) , 2018 .

[7]  John Salvatier,et al.  Probabilistic programming in Python using PyMC3 , 2016, PeerJ Comput. Sci..

[8]  N. Abrahamson,et al.  Alternative Approaches to Modeling Epistemic Uncertainty in Ground Motions in Probabilistic Seismic‐Hazard Analysis , 2014 .

[9]  David M. Boore,et al.  Path Durations for Use in the Stochastic‐Method Simulation of Ground Motions , 2014 .

[10]  J. Bommer,et al.  Application of Single-Station Sigma and Site-Response Characterization in a Probabilistic Seismic-Hazard Analysis for a New Nuclear Site , 2014 .

[11]  Robert R. Youngs,et al.  Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra , 2014 .

[12]  Linda Al Atik,et al.  Epistemic Uncertainty for NGA-West2 Models: , 2014 .

[13]  Norman A. Abrahamson,et al.  Summary of the ASK14 Ground Motion Relation for Active Crustal Regions , 2014 .

[14]  W. Silva,et al.  NGA-West2 Database , 2014 .

[15]  Jonathan P. Stewart,et al.  NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes , 2014 .

[16]  K. Campbell,et al.  NGA-West2 Ground Motion Model for the Average Horizontal Components of PGA, PGV, and 5% Damped Linear Acceleration Response Spectra , 2014 .

[17]  S. Akkar,et al.  Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East , 2014, Bulletin of Earthquake Engineering.

[18]  Rodolfo Puglia,et al.  Erratum to: Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset , 2014, Bulletin of Earthquake Engineering.

[19]  Julian J. Bommer,et al.  Challenges of Building Logic Trees for Probabilistic Seismic Hazard Analysis , 2012 .

[20]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[21]  Frank Scherbaum,et al.  Exploring the Proximity of Ground-Motion Models Using High-Dimensional Visualization Techniques , 2010 .

[22]  Julian J. Bommer,et al.  The Use and Misuse of Logic Trees in Probabilistic Seismic Hazard Analysis , 2008 .

[23]  BrianS-J. Chiou,et al.  An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra , 2008 .

[24]  Frank Scherbaum,et al.  The Estimation of Minimum-Misfit Stochastic Models from Empirical Ground-Motion Prediction Equations , 2006 .

[25]  David M. Boore,et al.  SEA99: A Revised Ground-Motion Prediction Relation for Use in Extensional Tectonic Regimes , 2005 .

[26]  C. Allin Cornell,et al.  Nonlinear Soil-Site Effects in Probabilistic Seismic-Hazard Analysis , 2004 .

[27]  E. M. Scordilis,et al.  Empirical Peak Ground-Motion Predictive Relations for Shallow Earthquakes in Greece , 2003 .

[28]  K. Campbell PREDICTION OF STRONG GROUND MOTION USING THE HYBRID EMPIRICAL METHOD AND ITS USE IN THE DEVELOPMENT OF GROUND-MOTION (ATTENUATION) RELATIONS IN EASTERN NORTH AMERICA , 2003 .

[29]  David M. Boore,et al.  Simulation of Ground Motion Using the Stochastic Method , 2003 .

[30]  D. Boore Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra , 1983 .

[31]  A. C. Miller,et al.  Discrete Approximations of Probability Distributions , 1983 .

[32]  H. Kanamori,et al.  A moment magnitude scale , 1979 .

[33]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[34]  M. A. Sandıkkaya,et al.  Erratum to: Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East , 2013, Bulletin of Earthquake Engineering.

[35]  R. Puglia,et al.  Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset , 2013, Bulletin of Earthquake Engineering.

[36]  David M. Boore,et al.  SMSIM — Fortran Programs for Simulating Ground Motions from Earthquakes: Version 2.3 — A Revision of OFR 96–80–A , 2000 .