Unsupervised characterization of site conditions from detailed information for improved ground motion modeling

Model development in the Next Generation Attenuation‐East (NGA‐East) project included two components developed concurrently and independently: (1) earthquake ground‐motion models (GMMs) that predict the median and aleatory variability of various intensity measures conditioned on magnitude and distance, derived for a reference hard‐rock site condition with an average shear‐wave velocity in the upper 30 m ( V S30 ) = 3000 m/s; and (2) a site amplification model that modifies intensity measures for softer site conditions. We investigate whether these models, when used in tandem, are compatible with ground‐motion recordings in central and eastern North America (CENA) using an expanded version of the NGA‐East database that includes new events from November 2011 (end date of NGA‐East data curation) to April 2022. Following this expansion, the data set has 187 events, 2096 sites, and 16,272 three‐component recordings, although the magnitude range remains limited (∼4 to 5.8). We compute residuals using 17 NGA‐East GMMs and three data selection criteria that reflect within‐CENA regional variations in ground‐motion attributes. Mixed‐effects regression of the residuals reveals a persistent pattern in which ground motions are overpredicted at short periods (0.01–0.6 s, including peak ground acceleration (PGA)) and underpredicted at longer periods. These misfits are regionally variable, with the Texas–Oklahoma–Kansas region having larger absolute misfits than other parts of CENA. Two factors potentially influencing these misfits are (1) differences in the site amplification models used to adjust the data to the reference condition during NGA‐East GMM development relative to CENA amplification models applied since the 2018 National Seismic Hazard Model (NSHM), and (2) potential bias in simulation‐based factors used to adjust ground motions from the hard‐rock reference condition to a V S30 = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.

The United States Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous US NSHM includes major changes to the underlying ground motion models (GMMs). Most of the changes are motivated by the new multi-period response spectra requirements of seismic design regulations that use hazard results for 22 spectral periods and 8 site classes. In the central and eastern United States (CEUS), the 2018 NSHM incorporates 31 new GMMs for hard-rock site conditions [Formula: see text], including the Next Generation Attenuation (NGA)-East GMMs. New aleatory variability and site-effect models, both specific to the CEUS, are applied to all median hard-rock GMMs. This article documents the changes to the USGS GMM selection criteria and provides details on the new CEUS GMMs used in the 2018 NSHM update. The median GMMs, their weights, epistemic uncertainty, and aleatory variability are compared with those considered in prior NSHMs. This article further provides implementation details on the CEUS site-effect model, which allows conversion of hard-rock ground motions to other site conditions in the CEUS for the first time in NSHMs. Compared with the 2014 NSHM hard-rock ground motions, the weighted average of median GMMs increases for large magnitude events at middle to large distance range, epistemic uncertainty increases in almost all situations, but aleatory variability is not significantly different. Finally, the total effect on hazard is demonstrated for an assumed earthquake source model in the CEUS, which shows an increased ring of ground motions in the vicinity of the New Madrid seismic zone and decreased ground motions near the East Tennessee seismic zone.

Ground motion models (GMMs) play a pivotal role in both deterministic and probabilistic seismic hazard assessments, which are essential for identifying the seismic safety of nuclear power plants. In regions with abundant seismic data, especially strong earthquake records, GMMs could be empirically derived. However, in areas like South Korea with scarce strong earthquake records, development of empirical GMMs is impractical, leading to the utilization of alternative methods such as stochastic simulations. There have been a few GMMs developed in South Korea, all of which relied on stochastically simulated motions. In this study, GMMs are developed for rock sites in South Korea using the hybrid empirical method (HEM) suggested by Campbell (2003). Western United States (WUS) is selected as a host region and five Next Generation Attenuation (NGA)-West2 GMMs are used as GMMs of the host region. The seismological parameters employed in the simulation, including effective point source distance, source and path duration, and path attenuation, duly encompass the findings of recent studies. The high-frequency spectral attenuation parameters, kappa, utilized as site attenuation parameters in ground motion simulations for the target region, are estimated in this study. It is primarily estimated using the classical method proposed by Anderson and Hough (1984). Additionally, the estimation process considers the standardized procedure and the recommended lower bound magnitude decisions put forth by Ktenidou et al. (2013) and Van Houtte et al. (2014), respectively. Since the shear wave velocity for bedrock is considered to be 760 m/s in South Korea, the site amplification functions have been applied with reference to this velocity for both the host and target regions. The adjustment factors obtained from simulated ground motions in both the host and target regions are applied to adjust NGA-West 2 Ground Motion Models (GMMs). Derived GMMs are for magnitudes from 5.0 to 7.5 and rupture distances from 10 to 500 km. Median GMMs are provided with aleatory standard deviations. Predictive GMMs are compared with observed ground motions from the available earthquake records for moment magnitudes 5.0 and 5.5. The notable advantages of the GMMs developed in this study are as follows: Distinct from previous researches utilizing stochastic methods, the implementation of HEM served to complement the limitations inherent in stochastic approaches such as lack of near-source ground motion characteristics. Defining the sites where GMMs are employed at Vs30 = 760m/s enables the derivation of seismic motions applicable to rock layers having Vs30 of 760m/s. Since aleatory standard deviations are quantitatively defined, they can serve as the sigma parameter within GMMs in Probabilistic Seismic Hazard Analysis (PSHA).

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.

Many studies have focused on horizontal ground motion, resulting in many coherency functions for horizontal ground motion while neglecting related problems arising from vertical ground motion. However, seismic events have demonstrated that the vertical components of ground motion sometimes govern the ultimate failure of structures. In this paper, a vertical coherency function model of spatial ground motion is proposed based on the Hao model and SMART 1 array records, and the validity of the model is demonstrated. The vertical coherency function model of spatial ground motion is also compared with the horizontal coherency function model, indicating that neither model exhibits isotropic characteristics. The value of the vertical coherency function has little correlation with that of the horizontal coherency function. However, the coherence of the vertical ground motion between a pair of stations decreases with their projection distance and the frequency of the ground motion. When the projection distance in the wave direction is greater than 800 meters, the coherency between the two points can be neglected.

Simulated ground motions play a crucial role in seismic engineering applications. The present study introduces a novel conditional generative adversarial networks (CGAN) based model to generate the time‐frequency representation of the ground motions and to use the iterative power and amplitude correction (IPAC) algorithm, referred to as the IPAC‐CGAN method, to simulate the nonstationary non‐Gaussian ground motions for scenario events. The proposed CGAN‐based model is trained using time‐frequency representations derived from recorded ground motions originating from strike‐slip fault earthquakes, obtained via the S‐transform. It effectively captures the power distribution in the time‐frequency domain for specific scenario events. Unlike prior GAN‐ or CGAN‐based models in the literature, which assume fixed durations across scenarios and lack control over the marginal probability density function (PDF) of sampled records, our approach innovatively adapts ground motion duration to earthquake magnitude, rupture distance, and site conditions. The IPAC‐CGAN method enables the generation of ground motions with durations that vary according to earthquake magnitude, rupture distance, and site conditions, which were usually fixed for all scenarios in the model developed based on GAN or CGAN in the literature. The combination of the IPAC algorithm and CGAN ensures accurate replication of the nonstationary, non‐Gaussian characteristics observed in actual seismic records, while in the literature, there is no control on the marginal PDF of the sampled record. Validation of the proposed method involves comparing pseudospectral acceleration statistics between simulated and actual ground motions. Predicted values from ground motion models are also considered for comprehensive assessment. Additionally, ductility demand comparisons for single‐degree‐of‐freedom systems using the Bouc–Wen hysteretic model are conducted using simulated and recorded ground motions. In all cases, the results show that the statistics of the linear and nonlinear responses of the simulated ground motions agree well with those of the recorded seismic ground motions. This validation underscores the effectiveness of the proposed method in accurately simulating realistic seismic ground motions.

Stochastic Modeling and Synthesis of Near-Fault Forward-Directivity Ground Motions

The Pacific Earthquake Engineering Research (PEER) Center next-generation attenuation relationships for subduction zone earthquakes (NGA-Sub) ground motion database is used to develop new conditional ground motion models (CGMMs), several scenario-based ground motion models (GMMs), and a traditional GMM to estimate the peak ground velocity ( PGV) for subduction zone (interface, intraslab) earthquakes. The PGV estimate in the CGMMs is conditioned on the rupture distance ( Rrup), magnitude ( Mw), time-averaged shear wave velocity in the top 30 m ( V s30) and pseudo-spectral acceleration PSA( T PGV). The period T PGV in the CGMMs is magnitude dependent to account for the magnitude dependence of the earthquake source corner frequency in the Fourier amplitude spectrum. Several scenario-based models are developed by combining the CGMM with PSA GMMs to directly estimate PGV given an earthquake scenario and site condition. Scenario-based models capture the complex ground motion effects in the underlying PSA GMMs and ensure the consistency with a design PSA spectrum, which is desired in engineering practice. In addition, a traditional PGV GMM is developed using Bayesian hierarchical regression. Finally, we compare all of these models and find that the scenario-based models are consistent with the traditional model developed in this study giving confidence to their use. The conditional and traditional PGV GMMs developed in this study benefit the performance-based design of engineering systems affected by subduction earthquakes when PGV is an important intensity measure.

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.

ABSTRACTIn this study, we develop a new nonergodic ground motion model (GMM) for Chile, which better captures the trade-off between the aleatory variability and epistemic uncertainty on ground motion estimates compared with existing GMMs. The GMM is developed for peak ground acceleration and pseudospectral acceleration at a period of 1 s. Most existing GMMs for subduction earthquake zones were developed based on an ergodic assumption, and this is not the exception for the subduction zone in Chile. Under the ergodic assumption, the ground motion variability at a given single site–source combination is considered the same as the variability observed in a global database. However, recent efforts have highlighted significant location-specific systematic and repeatable effects for ground motions recorded within a particular region. These systematic effects promote the relaxation of the ergodic assumption and the transition to the development of nonergodic GMMs. The nonergodic GMM developed in this study uses an ergodic GMM as a backbone, the systematic source and site effects are modeled using Gaussian processes, and the path effects are modeled using the cell-specific attenuation approach enhanced with a computer graphics-based algorithm. The coefficients of the nonergodic GMM are estimated using Bayesian inference via Markov chain Monte Carlo (MCMC) methods, in which we use an integrated nested Laplace approximation approach to address the computational burden involved in MCMC. The developed nonergodic GMM reveals spatially varying and correlated location-specific source, path, and site effects in Chile, which cannot be captured by existing Chilean ergodic GMMs. Moreover, the developed nonergodic GMM shows a reduced aleatory variability compared to existing ergodic GMMs that are commonly used in Chile. In addition, the developed nonergodic GMM shows small epistemic uncertainty for regions with large ground motion data and large epistemic uncertainty for regions with few ground motion data. Finally, we provide guidelines on how to use the developed nonergodic GMM in the context of probabilistic seismic hazard analysis, which is important for performance-based earthquake engineering assessments in Chile.

Considering the uncertainty and the time variation of frequency contents of real seismic excitation, a new versatile stochastic strong ground motion model named general stochastic seismic ground motion (GSSGM) model is presented in this paper. Some essential assumptions for the earthquake process used in this paper are first given. The intensity and energy of the target seismic ground motion are used to determine the model parameters. The frequency contents are demanded to be agreed with the main characteristics of the target ground motions. The GSSGM model is appropriate to simulate the stationary, intensity non-stationary and fully non-stationary stochastic processes. Additionally, a simple non-stationary stochastic seismic response analysis procedure based on the GSSGM model and the pseudo excitation theory is put forward. The presented non-stationary stochastic seismic response analysis procedure is later applied in the seismic response analysis of a real homogeneous earth dam. The non-stationary analysis results display the effects of non-stationarity on the seismic response of the dam and reflect the main phenomena of dynamic embankment-foundation interaction. The results indicate that the GSSGM model and the presented analysis procedure are effective.

A ground motion model (GMM) for interface subduction zone earthquakes of Northeast India (NEI) and its adjacent countries is developed for the first time. Countries adjacent to NEI are Bangladesh, Bhutan, China, Myanmar and Nepal. High-magnitude earthquakes occur frequently in these regions due to buildup of high-stress parameters in the subduction zone of the Indian tectonic plate. Strong motion data are too few and sparse to develop a robust GMM for this region. We used both finite-fault simulations and a stochastic point-source model in developing our GMM. In our GMM, we used 50,000 ground motion samples which were stochastically simulated for different moment magnitudes (Mw) of 5.0–9.0 and hypocentral distances of 30–300 km using a point-source seismological stochastic model and finite fault model. In this study, we calculated stress drop (∆σ), quality factor Q(f) and all other region-specific seismic input parameters from the past strong motion records of interface subduction zone earthquakes of NEI and its adjacent countries. We used these seismic input parameters in ground motion simulation. Sensitivity analyses of the input parameters were also performed to check the bias of the present model. Our GMM was validated by comparing it with the existing NEI interface strong motion records. We compared our GMM with other GMMs developed for interface subduction zone earthquakes for different regions in the world. We also compared our GMM with point-source and finite-fault simulation models. Ground motion parameters estimated using the point-source model are comparatively higher than the finite-fault simulation model. Horizontal components of peak ground acceleration (PGA) and spectral acceleration (Sa) can be estimated for NEI using our GMM.

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.

ABSTRACTThe seismic hazard of an area is determined based on the ground motion observed at that site. The intensity of the ground motion can be predicted using ground -motion models (GMMs). GMMs typically use distance metrics such as the Joyner–Boore distance (RJB) and the rupture distance (RRUP). However, apart from RJB and RRUP, probabilistic seismic hazard analysis (PSHA) also utilizes point-source-based distances like the epicentral distance (REPI) and the hypocentral distance (RHYP). These distance metrics are used for point sources when the fault geometry is unknown or is ignored. To obtain an accurate seismic hazard of an area, we need to determine the relationship between the distance metrics. In this study, we develop empirical relationships between RJB and various other distance metrics. This method avoids conducting computationally intensive tasks such as computing finite-fault-based distances for different fault geometry of a virtual rupture plane for each point source. The empirical equations provide the relation between RJB and target distance metric (Rtarget) based on the magnitude of the earthquake and the dip angle of the fault. In addition, we also require the depth to the top of the rupture to calculate RHYP. We discuss the steps to include the variability due to the conversion of the distance metrics in the PSHA. We have compared the results of this study with other published studies for distance conversion. A simple PSHA study of a circular area of 100 km using Pezeshk et al. (2011) as the GMM determined an increase in hazard using the proposed empirical equations and their uncertainties. The equations developed in this study can be directly applied in PSHA and are independent of the GMMs used for seismic hazard calculations. The equations can also be used for different fault geometries with a range of dip angles varying from 10° to 90°, for magnitudes 5.0–8.0, and distances up to 200 km. We have focused on the central and eastern United States.

Low‐frequency errors of a commonly used non‐stationary stochastic model (uniformly modulated filtered white‐noise model) for earthquake ground motions are investigated. It is shown both analytically and by numerical simulation that uniformly modulated filter white‐noise‐type models systematically overestimate the spectral response for periods longer than the effective duration of the earthquake, because of the built‐in low‐frequency errors in the model. The errors, which are significant for low‐magnitude short‐duration earthquakes, can be eliminated by using the filtered shot‐noise‐type models (i.e. white noise, modulated by the envelope first, and then filtered).