Ground Motion Models for rock sites in South Korea

In this study, we propose a new ground-motion model (GMM) for New Zealand (NZ) using a hybrid empirical method (HEM). Only a limited number of GMMs have been developed specifically using the NZ dataset. We utilized HEM to develop a GMM for shallow crustal earthquakes in NZ, aiming to predict PGA (peak ground acceleration) and PSA (pseudo spectral acceleration) at periods ranging from T = 0.01–10 s for moment magnitudes ( M w ) of 4.0 to 8.0, and a rupture distance ( R rup ) of up to 400 km, considering a reference site condition with V S30 = 760 m/s. We considered NZ the target region and Western North America (WNA) the host region. We utilized four GMMs from the NGA-West2 project for ground-motion modeling in WNA. A set of period-dependent adjustment factors were used to convert the GMMs from the host region to the target region. The adjustment factors were developed as the ratio between the stochastically simulated response spectrum in the target and host regions. Seismological parameters are required for stochastically simulating the response spectrum in both the host and target regions. These seismological parameters indicate the differences in source, path, and site effects between the two regions. The seismological parameters for target and host regions are adopted from Zhu et al. and Zandieh et al., respectively.

Abstract. Current practice in strong ground motion modelling for probabilistic seismic hazard analysis (PSHA) requires the identification and calibration of empirical models appropriate to the tectonic regimes within the region of application, along with quantification of both their aleatory and epistemic uncertainties. For the development of the 2020 European Seismic Hazard Model (ESHM20) a novel approach for ground motion characterisation was adopted based on the concept of a regionalised scaled-backbone model, wherein a single appropriate ground motion model (GMM) is identified for use in PSHA, to which adjustments or scaling factors are then applied to account for epistemic uncertainty in the underlying seismological properties of the region of interest. While the theory and development of the regionalised scaled-backbone GMM concept have been discussed in earlier publications, implementation in the final ESHM20 required further refinements to the shallow-seismicity GMM in three regions, which were undertaken considering new data and insights gained from the feedback provided by experts in several regions of Europe: France, Portugal and Iceland. Exploration of the geophysical characteristics of these regions and analysis of additional ground motion records prompted recalibrations of the GMM logic tree and/or modifications to the proposed regionalisation. These modifications illustrate how the ESHM20 GMM logic tree can still be refined and adapted to different regions based on new ground motion data and/or expert judgement, without diverging from the proposed regionalised scaled-backbone GMM framework. In addition to the regions of crustal seismicity, the scaled-backbone approach needed to be adapted to earthquakes occurring in Europe's subduction zones and to the Vrancea deep seismogenic source region. Using a novel fuzzy methodology to classify earthquakes according to different seismic regimes within the subduction system, we compare ground motion records from non-crustal earthquakes to existing subduction GMMs and identify a suitable-backbone GMM for application to subduction and deep seismic sources in Europe. The observed ground motion records from moderate- and small-magnitude earthquakes allow us to calibrate the anelastic attenuation of the backbone GMM specifically for the eastern Mediterranean region. Epistemic uncertainty is then calibrated based on the global variability in source and attenuation characteristics of subduction GMMs. With the ESHM20 now completed, we reflect on the lessons learned from implementing this new approach in regional-scale PSHA and highlight where we hope to see new developments and improvements to the characterisation of ground motion in future generations of the European Seismic Hazard Model.

In this study, we develop ground-motion models (GMMs) for the Island of Hawaii. This area has been the site of several significant earthquake events with a growing database of strong ground-motion observations. Ground-motion modeling on the Island of Hawaii is challenging due to different anelastic attenuation characteristics, the volcanic origin of some of the events, and event depth distribution. Only a few GMMs have been developed for the Island of Hawaii. In this study, we apply a hybrid empirical method (HEM) to develop two separate GMMs for shallow (hypocentral depth ≤ 20 km) and deep (hypocentral depth > 20 km) earthquakes on the Island of Hawaii. We utilize the ratio of the stochastic point-source model in the target and host regions as an appropriate adjustment factor. We apply these adjustment factors to convert the GMMs from the host (western North America) to the target (Island of Hawaii) region. We considered five GMMs proposed in the Next Generation Attenuation Phase 2 project by the Pacific Earthquake Engineering Research Center to model ground motions in the host region. We developed GMMs to predict peak ground acceleration and 5%-damped pseudospectral acceleration at periods T = 0.01–10 s, for moment magnitudes (M) in the range of 3–7.5, and for Joyner–Boore distances in the RJB≤200 km range. The applicability of HEM to develop GMMs for the Island of Hawaii and the growing strong ground-motion data result in further improvements in the capability of GMMs.

Selection of earthquake ground motion models using the deviance information criterion

Effects of different empirical ground motion models on seismic hazard maps for North Iceland

Research Article| November 26, 2013 An Evaluation of Eastern North American Ground‐Motion Models Developed Using the Hybrid Empirical Method Kenneth W. Campbell Kenneth W. Campbell EQECAT, Inc., 1030 NW 161st Place, Beaverton, Oregon 97006kcampbell@eqecat.com Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2014) 104 (1): 347–359. https://doi.org/10.1785/0120120256 Article history first online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Kenneth W. Campbell; An Evaluation of Eastern North American Ground‐Motion Models Developed Using the Hybrid Empirical Method. Bulletin of the Seismological Society of America 2013;; 104 (1): 347–359. doi: https://doi.org/10.1785/0120120256 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract Ground‐motion models (GMMs) and ground‐motion adjustment factors developed using the hybrid empirical method (HEM) are used in seismic‐hazard analyses throughout the world as an alternative to GMMs developed from the more traditional empirical and simulation methods. The HEM uses the ratio of stochastic ground‐motion simulations between a target and host region to adjust empirical GMMs from the host region to use in the target region. The HEM is used primarily in regions where strong‐motion data are sparse or exist only for small‐magnitude earthquakes. The most common application of the HEM has been in the development of GMMs for eastern North America (ENA), two of which were used in the 2008 U.S. national seismic‐hazard maps, but the method also has been used to develop or adjust GMMs in many other regions of the world. A comparison of four ENAGMMs developed using the HEM and a fifth developed using the closely related referenced empirical approach show that they fall into three distinct groups based on differences in the methods, models, and parameters used to calculate the host‐to‐target adjustment factors, and on differences in the selection of the host empirical ground‐motion models. A different set of groups are implied from the aleatory variability models. General guidance is provided to aid the user in the selection and weighting of the five GMMs for application in seismic‐hazard analysis. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Two methods were developed to estimate updated mean seismic hazard for existing probabilistic seismic hazard analyses (PSHAs) due to a change in the ground motion model (GMM). Both methods were used to estimate updated hazard at nuclear power plant (NPP) sites in the Central and Eastern United States (CEUS) for a change from the Electric Power Research Institute (EPRI) 2013 GMM to the Next Generation Attenuation (NGA)‐East GMM. These methods present efficient tools to inform decisions on whether to perform a full PSHA revision or other detailed evaluations, especially when a large number of sites must be analyzed. A Simplified Hazard (SiHaz) method was developed to estimate mean hazard explicitly using a reduced PSHA logic tree that incorporates the updated GMM and potential changes in the site response model. An alternative scaling method was independently developed to be applied directly to current CEUS NPP hazard. Both methods were validated using updated PSHA results at several sites. Estimates at 46 NPP sites using both methods showed good agreement for mean annual frequencies of exceedance between 1E‐4 and 1E‐5/yr.

The backbone approach to constructing a ground‐motion logic tree for probabilistic seismic hazard analysis (PSHA) can address shortcomings in the traditional approach of populating the branches with multiple existing, or potentially modified, ground‐motion models (GMMs) by rendering more transparent the relationship between branch weights and the resulting distribution of predicted accelerations. To capture epistemic uncertainty in a tractable manner, there are benefits in building the logic tree through the application of successive adjustments for differences in source, path, and site characteristics between the host region of the selected backbone GMM and the target region for which the PSHA is being conducted. The implementation of this approach is facilitated by selecting a backbone GMM that is amenable to such host‐to‐target adjustments for individual source, path, and site characteristics. The NGA‐West2 GMM of Chiou and Youngs (CY14) has been identified as a highly adaptable model for crustal seismicity that is well suited to such adjustments. Rather than using generic source, path, and site characteristics assumed appropriate for the host region, the final suite of adjusted GMMs for the target region will be better constrained if the host‐region parameters are defined specifically on the basis of their compatibility with the CY14 backbone GMM. To this end, making use of a recently developed crustal shear‐wave velocity profile consistent with CY14, we present an inversion of the model to estimate the key source and path parameters, namely the stress parameter and the anelastic attenuation. With these outputs, the effort in constructing a ground‐motion logic tree for any PSHA dealing with crustal seismicity can be focused primarily on the estimation of the target‐region characteristics and their associated uncertainties. The inversion procedure can also be adapted for any application in which different constraints might be relevant.

In this study, the hybrid empirical method (HEM) was used to develop a new ground-motion model (GMM) for the Gulf Coast of the United States. We used five new empirical GMMs developed by the Pacific Earthquake Engineering Research Center for the Next Generation Attenuation-West2 project to estimate ground-motion intensity measures (GMIMs) in the host region. The new GMM is derived for the horizontal peak ground acceleration and response-spectral ordinates at periods ranging from 0.01 to 10 s, moment magnitudes ranging from M 3.5 to 8.0, and rupture distance (RRUP) as far as 1000 km from the site, although the GMMs are the best constrained for RRUP<300–400 km. The predicted GMIMs are for a reference site defined as the Gulf Coast region hard rock with VS30=3000 m/s and κ0=0.006 s, in which VS30 is the time-averaged shear-wave velocity in the top 30 m of the site profile, and κ0 is the total attenuation of the ground motion as it propagates through the site profile. Seismological parameters used to derive the GMIM stochastic estimates in the Gulf Coast target region are adopted from the most recent research and published information for the region. The seismological parameters for the western North America host region are adopted from Zandieh et al. (2017). The proposed GMM is compared with the Pezeshk et al. (2018) model, which is also a HEM approach, and was developed for central North America and excluded the Gulf Coast region to show the differences between the regions.

One of the main objectives in engineering seismology or in seismic hazard studies is to estimate the possible ground motion for a given earthquake scenario. In the sparse data regions mostly the ground motion models (GMM) are developed using either seismological models or hybrid empirical approaches. However, if these GMMs do not accommodate the regional seismological attributes, a large uncertainty in ground motion estimates is possible. To overcome this concern, scaling 5% damped Pseudo Spectral Acceleration (PSA) from Fourier amplitude spectra (FAS) proves to be physically consistent as it can capture both spatial and temporal characteristics of the ground motion. Hence, the present study aims to develop a GMM for PSA using FAS and significant duration as the predictor variables. However, since there are few GMMs available for FAS, the current study also aims to develop a GMM for FAS using the earthquake parameters as the predictor variables for intraplate regions. This article employs an Artificial Neural Network (ANN) to develop both GMMs using the Next Generation Attenuation (NGA)‐East database for both horizontal (Effective Amplitude spectra for FAS and RotD50 component for PSA) and vertical components. To verify the performance of the developed models, the residuals analysis and parametric studies have been performed. The parametric study shows that the GMMs can capture the magnitude and distance scaling consistent with the observations. Further, the PSA GMM compared with the global GMMs and it is observed that the predictions lie well within the median of all the available models, proving the models’ effectiveness in estimating the ground motion predictions for future data. The developed model can be used only within the considered ranges of the predicted variables such as rupture distances between [19.05–1000] km, the Mw ranges from [3.12–5.74] with Vs30 in the range of [209–2000] m/s.

Ground motion models (GMMs) are the key element of probabilistic seismic hazard assessment (PSHA). These models predict an earthquake ground motion intensity measure as a function of some independent variables that represent the earthquake source, path and site effects. The latter one can substantially influence the PSHA results since they are critical factors affecting the amplitude, frequency content, and duration of seismic waves. In regions with different geological characteristics, therefore, site effects should be appropriately accounted for in GMMs and thus PSHA. This is the case for southwest Iceland where due to its distinct geological characteristics (e.g., lava layers interbedded with sedimentary deposits), site-specific amplification varies significantly across different geological units, particularly at higher frequencies. In this study we develop a new set of GMMs with frequency-dependent site amplification factors for four site categories (i.e., hard rock, rock, lava, and stiff soil) in Iceland. We use advanced Bayesian hierarchical modeling (BHM). The BHM framework reduces uncertainty and enhances the reliability of predictions. The models also benefit from the inclusion of site-specific parameters, such as frequency-dependent amplification factors, which account for Iceland’s unique geological conditions. Moreover, due to the limited strong-motion data in Iceland, particularly for larger magnitudes, developing GMMs with all the essential terms such as magnitude saturation, magnitude-dependent distance scaling and inelastic attenuation terms, is challenging. In this regard, the Bayesian statistical approach can be particularly useful as it allows taking prior information about the model parameters into account and adding it to the information in the likelihood that stems from the observed data. This study utilized 83 strong-motion records from six strike-slip earthquakes (*Mw* 5.1–6.5), recorded at 34 seismic stations across diverse geological settings. In GMMs, uncertainty in the median prediction values is epistemic which depends on the GMM’s functional form. To consider the epistemic uncertainty therefore, we take different functional forms as those used and proposed in Kowsari et al. (2020), with a new functional form specifically calibrated to Icelandic strong-motions that incorporates depth-dependent magnitude scaling and frequency-independent site effects to improve the applicability of GMMs in Iceland. Later, this epistemic uncertainty can be taken into account in PSHA using either a backbone or logic tree approach. The Bayesian hierarchical ground motion models (BH-GMMs) developed in this study consider the attenuation of BH-GMMs for peak ground acceleration (*PGA*) at four site classes along with the observed Icelandic strong-motions for different magnitudes (*Mw* 5.2, *Mw* 6.4, *Mw* 7.2). The results indicated the capability of BH-GMMs to effectively capture the attenuation of *PGA* with distance for varying magnitudes. Moreover, the attenuation trends of observed PGAs, align closely with the model predictions for each geological unit, underscoring the importance of incorporating site-specific amplification in PSHA. The distinction in amplification behaviors, particularly at higher frequencies, provides critical insights into the interaction between seismic waves and geological units. This study highlights the critical role of local geological conditions in shaping seismic responses and sets a benchmark for future research in seismic hazard assessment, particularly in regions with complex geological and seismic environments like Southwest Iceland. Kowsari M, Sonnemann T, Halldorsson B, Hrafnkelsson B, Snæbjörnsson JÞ, Jónsson S. Bayesian Inference of Empirical Ground Motion Models to Pseudo-Spectral Accelerations of South Iceland Seismic Zone Earthquakes based on Informative Priors. Soil Dyn Earthq Eng 2020;132:106075. https://doi.org/10.1016/j.soildyn.2020.106075.

With the improvement of the world’s largest seafloor observation network (S-net) and the increase in the quantity and quality of records, the ergodic assumptions can be further relaxed in the modeling of offshore ground motion models (GMMs). This allows accounting for systematic and repeatable source, site, and path effects to further understand the characteristics of offshore ground motion in the Japan Trench region. We developed an offshore ergodic backbone GMM for subduction earthquakes and classified the sites into four categories using horizontal–vertical response spectral ratio to investigate site amplification. The offshore ergodic GMM is applicable for subduction earthquake scenarios with moment magnitudes ranging from 4.0 to 7.4 and rupture distances ranging from 10 to 300 km. Comparing offshore ergodic GMMs with onshore GMMs for subduction earthquakes, we found that offshore GMMs were significantly different from onshore GMMs, especially in the long-period and unburied states. Then a new offshore non-ergodic GMM was developed based on the offshore ergodic GMM. The systematic and repeatable source and site effects were captured by the spatially varying coefficients represented by Gaussian processes, while the systematic and repeatable path effects were captured by cell-specific anelastic attenuation proposed by Dawood and Rodriguez-Marek (2013), calculated with the Cohen-Sutherland computer graphics algorithm. The non-ergodic GMM revealed systematic and repeatable source, site, and path effects that were not captured by the ergodic GMM. Moreover, the non-ergodic GMM showed reduced aleatory variability and epistemic uncertainty on ground motion estimation compared to ergodic GMM. The reduction of aleatory variability and epistemic uncertainty had a significant impact on probabilistic seismic hazard analysis. Quantifications of these results are contributed to conduct reasonable seismic design and seismic risk assessment for marine engineering in offshore regions vulnerable to strong subduction earthquakes.

This document is the final project report of the Next Generation Attenuation for Central and Eastern North America (CENA) project (NGA-East). The NGA-East objective was to develop a new ground-motion characterization (GMC) model for the CENA region. The GMC model consists of a set of new ground-motion models (GMMs) for median and standard deviation of ground motions and their associated weights to be used with logic-trees in probabilistic seismic hazard analyses (PSHA). NGA-East is a large multidisciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER), at the University of California. The project has two components: (1) a set of scientific research tasks, and (2) a model-building component following the framework of the “Seismic Senior Hazard Analysis Committee (SSHAC) Level 3” (Budnitz et al. 1997; NRC 2012). Component (2) is built on the scientific results of component (1) of the NGA-East project. This report documents the tasks under component (2) of the project. Under component (1) of NGA-East, several scientific issues were addressed, including: (a) development of a new database of ground motion data recorded in CENA; (b) development of a regionalized ground-motion map for CENA, (c) definition of the reference site condition; (d) simulations of ground motions based on different methodologies; and (e) development of numerous GMMs for CENA. The scientific tasks of NGA-East were all documented as a series of PEER reports. The scope of component (2) of NGA-East was to develop the complete GMC. This component was designed as a SSHAC Level 3 study with the goal of capturing the ground motions’ center, body, and range of the technically defensible interpretations in light of the available data and models. The SSHAC process involves four key tasks: evaluation, integration, formal review by the Participatory Peer Review Panel (PPRP), and documentation (this report). Key tasks documented in this report include review and evaluation of the empirical ground- motion database, the regionalization of ground motions, and screening sets of candidate GMMs. These are followed by the development of new median and standard deviation GMMs, the development of new analyses tools for quantifying the epistemic uncertainty in ground motions, and the documentation of implementation guidelines of the complete GMC for PSHA computations. Appendices include further documentation of the relevant SSHAC process and additional supporting technical documentation of numerous sensitivity analyses results. The PEER reports documenting component (1) of NGA-East are also considered “attachments” to the current report and are all available online on the PEER website (https://peer.berkeley.edu/). The final NGA-East GMC model includes a set of 17 GMMs defined for 24 ground-motion intensity measures, applicable to CENA in the moment magnitude range of 4.0 to 8.2 and covering distances up to 1500 km. Standard deviation models are also provided for site-specific analysis (single-station standard deviation) and for general PSHA applications (ergodic standard deviation). Adjustment factors are provided for consideration of source-depth effects and hanging-wall effects, as well as for hazard computations at sites in the Gulf Coast region.

Limited availability of recorded ground motions poses a challenge for reliable probabilistic seismic-hazard analysis (PSHA), even in highly seismic regions like the Western United States. Stochastic ground motions are commonly employed to address this challenge. However, the stochastic ground motion models (GMMs) may not consistently generate ground motions compatible with the site hazard due to their calibration using global data, failing to capture site-specific characteristics adequately. In the absence of recorded motions, physics-informed simulations provide a viable alternative but are deterministic with limitations of their own that makes them challenging to support PSHA. This article introduces a Bayesian framework that combines prior knowledge from a stochastic GMM, calibrated with global data, with site-specific data obtained from deterministic physics-informed simulations. The proposed framework utilizes the Rezaeian–Der Kiureghian (2010) model as the stochastic GMM and incorporates site-specific data from the CyberShake 15.12 study. By updating the mean and variance of the predictive relationships, along with the marginal distribution of the model parameters, through Bayesian inference, this framework allows for the simulation of site-specific ground motions consistent with the site characteristics. The statistics of peak ground acceleration distributions, as well as both the median and variability of the elastic response spectra, obtained from the calibrated stochastic GMM, demonstrate consistency with those derived using GMMs based on the Next Generation Attenuation (NGA) database.

The purpose of this report is to provide a set of ground motion models (GMMs) to be considered by the U.S. Geological Survey (USGS) for their National Seismic Hazard Maps (NSHMs) for the Central and Eastern U.S. (CEUS). These interim GMMs are adjusted and modified from a set of preliminary models developed as part of the Next Generation Attenuation for Central and Eastern North-America (CENA) project (NGA-East). The NGA-East objective was to develop a new ground-motion characterization (GMC) model for the CENA region. The GMC model consists of a set of GMMs for median and standard deviation of ground motions and their associated weights in the logic-tree for use in probabilistic seismic hazard analysis (PSHA). NGA-East is a large multidisciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER), at the University of California, Berkeley. The project has two components: (1) a set of scientific research tasks, and (2) a model-building component following the framework of the “Seismic Senior Hazard Analysis Committee (SSHAC) Level 3” [Budnitz et al. 1997; NRC 2012]. Component (2) is built on the scientific results of component (1) of the NGA-East Project. This report does not document the final NGA-East model under (2), but instead presents interim GMMs for use in the U.S. Geological Survey (USGS) National Seismic Hazard Maps. Under component (1) of NGA-East, several scientific issues were addressed, including: (a) development of a new database of empirical data recorded in CENA; (b) development of a regionalized ground-motion map for CENA, (c) definition of the reference site condition; (d) simulations of ground motions based on different methodologies, (e) development of numerous GMMs for CENA, and (f) the development of the current report. The scientific tasks of NGA- East were all documented as a series of PEER reports. This report documents the GMMs recommended by the authors for consideration by the USGS for their NSHM. The report documents the key elements involved in the development of the proposed GMMs and summarizes the median and aleatory models for ground motions along with their recommended weights. The models presented here build on the work from the authors and aim to globally represent the epistemic uncertainty in ground motions for CENA. The NGA-East models for the USGS NSHMs includes a set of 13 GMMs defined for 25 ground-motion intensity measures, applicable to CENA in the moment magnitude range of 4.0 to 8.2 and covering distances up to 1500 km. Standard deviation models are also provided for general PSHA applications (ergodic standard deviation). Adjustment factors are provided for hazard computations involving the Gulf Coast region.