Ground Motion Model Research Articles

Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models

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 ( VS30) = 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 VS30 = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.

Constraining Between-Event Variability of Kinematic Rupture Scenarios by Empirical Ground-Motion Model: A Case Study in Central Italy

ABSTRACT The region of central Italy is well known for its moderate-to-large earthquakes. Events such as 2016 Mw 6.2 Amatrice, generated in the shallow extensional tectonic regime, motivate numerical simulations to gain insights into source-related ground-motion complexities. We utilize a hybrid integral–composite kinematic rupture model by Gallovič and Brokešová (2007) to predict ground motions for other hypothetical Amatrice fault rupture scenarios (scenario events). The synthetic seismograms are computed in 1D crustal velocity models, including region-specific 1D profiles for selected stations up to 10 Hz. We create more than ten thousand rupture scenarios by varying source parameters. The resulting distributions of synthetic spectral accelerations at periods 0.2–2 s agree with the empirical nonergodic ground-motion model of Sgobba et al. (2021) for central Italy in terms of the mean and total variability. However, statistical mixed-effect analysis of the residuals indicates that the between-event variability of the scenarios exceeds the empirical one significantly. We quantify the role of source model parameters in the modeling and demonstrate the pivotal role of the so-called stress parameter that controls high-frequency radiation. We propose restricting the scenario variability to keep the between-event variability within the empirical value. The presented validation of the scenario variability can be generally utilized in scenario modeling for more realistic physics-based seismic hazard assessment.

Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

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 assessment of probabilistic seismic risk using ground-motion simulations via a Monte Carlo approach

Accurately characterizing ground motions is crucial for estimating probabilistic seismic hazard and risk. The growing number of ground-motion models, and increased use of simulations in hazard and risk assessments, warrants a comparison between the different techniques available to predict ground motions. This research aims at investigating how the use of different ground-motion models can affect seismic hazard and risk estimates. For this purpose, a case study is considered with a circular seismic source zone and two line sources. A stochastic ground-motion model is used within a Monte Carlo analysis to create a benchmark hazard output. This approach allows the generation of many records, helping to capture details of the ground-motion median and variability, which a ground motion prediction equation may fail to properly model. A variety of ground-motion models are fitted to the simulated ground motion data, with fixed and magnitude-dependant standard deviations (sigmas) considered. These include classic ground motion prediction equations (with basic and more complex functional forms), and a model using an artificial neural network. Hazard is estimated from these models and then we extend the approach to a risk assessment for an inelastic single-degree-of-freedom-system. Only the artificial neural network produces accurate hazard results below an annual frequency of exceedance of 1 × 10–3 years−1. This has a direct impact on risk estimates—with ground motions from large, close-to-site events having more influence on results than expected. Finally, an alternative to ground-motion modelling is explored through an observational-based hazard assessment which uses recorded strong-motions to directly quantify hazard.

The 2023 US National Seismic Hazard Model: Ground-motion characterization for the conterminous United States

We update the ground-motion characterization for the 2023 National Seismic Hazard Model (NSHM) for the conterminous United States. The update includes the use of new ground-motion models (GMMs) in the Cascadia subduction zone; an adjustment to the central and eastern United States (CEUS) GMMs to reduce misfits with observed data; an updated boundary for the application of GMMs for shallow, crustal earthquakes in active tectonic regions (i.e. western United States (WUS)) and stable continental regions (i.e. CEUS); and the use of improved models for the site response of deep sedimentary basins in the WUS and CEUS. Site response updates include basin models for the California Great Valley and for the Portland and Tualatin basins, Oregon, as well as long-period basin effects from three-dimensional simulations in the Greater Los Angeles region and in the Seattle basin; in the CEUS, we introduce a broadband (0.01- to 10-s period) amplification model for the effects of the passive-margin basins of the Atlantic and Gulf Coastal Plains. In addition, we summarize progress on implementing rupture directivity models into seismic hazard models, although they are not incorporated in the 2023 NSHM. We implement the ground-motion characterization for the 2023 NSHM in the US Geological Survey’s code for probabilistic seismic hazard analysis, nshmp-haz-v2, and present the sensitivity of hazard to these changes. Hazard calculations indicate widespread effects from adjustments to the CEUS GMMs, from the incorporation of Coastal Plain amplification effects, and from the treatment of shallow-basin and out-of-basin sites in the San Francisco Bay Area and Los Angeles region, as well as locally important changes from subduction-zone GMMs, and from updated and new WUS basins.

Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

ABSTRACT Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm’s potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.

Development of an empirical ground-motion model for post-mining induced seismicity near Gardanne, France

Development of an empirical ground-motion model for post-mining induced seismicity near Gardanne, France

Nonergodic Ground-Motion Models for Subduction Zone and Crustal Earthquakes in Japan

ABSTRACT We investigate the nonergodic behavior of ground motions from subduction zone earthquakes and crustal earthquakes in Japan using the National Research Institute for Earth Science and Disaster Resilience strong-motion flatfile together with various reference ergodic ground-motion models (GMMs). For the nonergodic path effects, our nonergodic GMM has two path effects terms: a cell-specific linear-distance scaling, denoted as δP2PQ, that mimics the effects of a 3D Q structure, and a source- and site-specific term related to the effects of the 3D velocity structure, denoted δP2PV. The resulting model provides spatially varying nonergodic source, site, and path terms. The GMM smoothly interpolates and extrapolates the nonergodic terms in space so that the GMM can be applied to any combination of source and site locations in the region. In regions where data are sparse, the median nonergodic terms will approach zero but with large epistemic uncertainty. Over the period range of 0–10 s, the ranges of the standard deviations of the nonergodic source, path, and site terms are 0.2–0.65, 0.25–0.40, and 0.15–0.40 natural log units, respectively. The fully nonergodic models lead to a 40%–55% decrease in the aleatory standard deviation compared to the reference ergodic GMMs. This large reduction in the aleatory standard deviation combined with the change in the median given by the nonergodic terms can exert a significant impact on the computed seismic hazard for the Japan region.

An equivalent point-source stochastic model of the NGA-East ground-motion models

The main objective of this study is to estimate seismological parameters in Central and Eastern North America (CENA), including the geometrical spreading, anelastic attenuation, stress parameter, and site attenuation parameters. In this study, we use particle swarm optimization (PSO) to invert a weighted average of the median 5%-damped pseudo-spectral acceleration (PSA) predicted from the Next Generation Attenuation-East (NGA-East) ground-motion models (GMMs) to develop a point-source stochastic GMM with a well-constrained set of ground-motion parameters. Magnitude-specific inversions are performed for moment magnitude ranges M = 4.0–8.0, rupture distances Rrup = 1–1000 km, and periods T = 0.01–10 s, and National Earthquake Hazard Reduction Program site class A conditions. The result of this study yields a single stochastic GMM that yields PSA values similar to the median NGA-East GMMs. The parameters derived from this study can be used for the hybrid empirical method (HEM) applications. This study is the first to perform a formal inversion using the GMMs developed for the NGA-East project. The approach has been validated using simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs.

Regionally adjusted stochastic earthquake ground motion models, associated variabilities and epistemic uncertainties

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region’s seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region’s ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters.

Reducing Uncertainty in Ground Motion Models

Reducing Uncertainty in Ground Motion Models

Comparison and selection of ground motion prediction equations for the Sichuan–Yunnan area, southwest China

The Sichuan–Yunnan area is one of the most seismically active regions in China. As ground-motion models form a key component of seismic hazard analysis, it is important to select (or develop) appropriate models for this area. The increasing number of digital ground-motion records of earthquakes in this area has allowed the development of local ground motion prediction equations (GMPEs). This study compares and, later, recommends appropriate GMPEs for the Sichuan–Yunnan area. We first evaluate the inherent quality of local GMPEs, with respect to their underlying datasets, the variables used and their functional forms, to determine a set of candidate GMPEs. Then we investigate how well the predictions from the GMPEs match observations computed from strong-motion records of recent earthquakes in this area. The fit between predictions and observations varies significantly amongst the GMPEs. The results suggest that some recent local GMPEs would lead to biased ground-motion estimates due to limitations of their underlying datasets and functional forms. Based on both evaluations of inherent quality and compatibility with observations, only one local GMPE is recommended. A comparison of the predictions from three widely-used non-local GMPEs indicates that ground motions in the Sichuan–Yunnan area appear more variable than those in other regions but that predictions from these non-local GMPEs are generally unbiased. We recommend use of a mixture of robust local and non-local GMPEs within seismic hazard analyses to capture the epistemic uncertainty in ground-motion prediction for this area.

Compatible Ground Motion Models for South Korea Using Moderate Earthquakes

Due to a heightened interest in the field of earthquakes after two moderately sized earthquakes occurred in Gyeongju and Pohang, this study explores which ground motion prediction equations are compatible for the South Korea region. Due to data availability, ground motions from five earthquakes of moderate magnitude were used for comparing against selected ground motion models. Median rotated response spectral ordinates at a period of 0.2 s were extracted from these ground motions, which served as a basis for comparison. Twelve ground motion models were considered from the Next Generation Attenuation West, West2, and East programs due to their extensive databases and robust analytical techniques. A comparison of relative residuals, z-score, and each event found that the subset of Next Generation Attenuation—East ground motion prediction equations did not perform as well as the suite of Next Generation Attenuation—West2 ground motion prediction equations, most likely due to the regional simulations involved in developing the database. Interestingly, the ground motion models that performed relatively well were from the set designed for rock conditions.

Application of GK17 Ground-Motion Model to Preliminary Processed Turkish Ground-Motion Recordings Dataset and GK Model Adjustment to the Turkish Environment by Developing Partially Nonergodic Model

Abstract The global ergodic GK17 (Graizer, 2018) ground-motion model developed using the Next Generation Attenuation-West2 database (Ancheta et al., 2014) for the active crustal regions (ACRs) was applied to the dataset of recordings from the two largest moment magnitude Mw 7.8 and 7.5 earthquakes in Türkiye (Buckreis et al., 2023a). The GK17 model demonstrates acceptable performance while mostly underpredicting spectral accelerations (SAs) at near-fault up to ∼100 km and far-field more than ∼400 km rupture distances for periods T < 1 s. The GK17 model was modified by applying additional Rrup distance and VS30 residuals corrections, creating an updated GK nonergodic model tuned for Türkiye called the GK_T model. After varying anelastic attenuation of SAs QSA, it was concluded that the average for the ACRs value of QSA=120 produces the best results. Comparisons of the model site amplification functions (SAFs) show that the GK_T SAFs are lower compared to the GK17 model. The Türkiye-specific GK_T partially nonergodic model shows better agreement with the recorded data than the ergodic GK17 model, especially at short periods and short rupture distances. As expected, application of the Türkiye-specific model results in a lower sigma. It is also lower than the within-event sigma of the GK17 model, except for the period range of 1.5 < T < 3 s. Improvements to the GK_T model can be expected with an addition of deep sediment (basin depth) information to the current dataset and other earthquakes in the region.

On the Susceptibility of Reinforced Concrete Beam and Rigid-Frame Bridges Subjected to Spatially Varying Mining-Induced Seismic Excitation.

This paper aims to identify the optimal reinforced concrete bridge construction for regions at risk of mining-induced seismic shocks. This study compares the performances of two common bridge types made of the same structural tissue, i.e., a reinforced concrete beam bridge and rigid-frame bridge under real mining-induced tremors using uniform and spatially varying ground motion models. This study investigates the dynamic responses of the bridges depending on wave velocity and assesses their susceptibility to mining-triggered tremors based on the contribution of quasi-static and dynamic effects in the global dynamic responses of the bridges. This study revealed significant changes in dynamic response under spatially varying ground excitation for both bridge types. It was observed that rigid-frame bridges show higher susceptibility to quasi-static effects due to their stiffness, whereas beam bridges are more susceptible to dynamic stresses. This study recommends that in regions with mining tremors, the choice between bridge types should consider the possibility of limiting individual components of stress. A solution may involve the reduction in quasi-static components through structural reinforcement or decreasing dynamic components by using vibration absorbers. It was found that beam bridges are more cost-effective and practical in mining-affected areas, especially when founded on weak grounds.

Deterministic ground motion modeling with target earthquakes and site effects in eastern Azerbaijan

In the context of assessing seismic hazard, accurately predicting ground motion stands out as a crucial task. Achieving precision in ground motion modeling proves valuable in revealing the actual pattern, even when faced with insufficient data on soil structure, provided there is precise information about the seismic source. This study introduces a methodology for calculating local- and near-field ground motion, expressed in peak ground acceleration (PGA) and intensity values. The deterministic approach is employed, incorporating source characteristics and one-dimensional (1D) site effects. For the chosen test area, the Ismayilli-Shamakhi region on the southeastern slope of the Greater Caucasus in Azerbaijan, two seismic scenarios are investigated: the 1902 Shamakhi earthquake (magnitude M = 6.8) and the November 25, 2000 Baku-Caspian earthquake (two shocks with moment magnitude Mw = 6.08 and 6.18). Different soil types are considered to validate the proposed methodological procedures. The analysis involves the computation of peak ground acceleration motion for two scenario earthquakes: a local-field event with M = 6.8 and a near-field event with Mw = 6.5, representing the average magnitude of the 2000 Baku-Caspian earthquake. The computed peak ground acceleration values are then used to derive intensities. Notably, the 1902 Shamakhi earthquake and the 2000 Baku-Caspian earthquake exhibit similar trends on surface PGA values. The local-field scenario estimates PGA values ranging from 77 to 328 gal, corresponding to MSK-64 scale intensity levels of VII-IX. The near-field scenario, with PGA values ranging from 28 to 62 gal, aligns with MSK-64 intensity levels of VI–VII. In the final assessment, the amplification factor in the study area varies between 0.55 and 0.83. The seismic hazard level is identified as high in the southern and southeastern regions, particularly in areas with soft shallow and medium-depth soils, indicating a high potential for ground motion amplification.

The 3D Physics-Based Broadband Ground-Motion Simulation, including Topography Effects Causing Damage to Structures, for the 2022 Mw 6.6 Menyuan, China, Earthquake

ABSTRACT On 8 January 2022, Menyuan County, China, was struck by an Mw 6.6 earthquake that caused surface rupture in the epicentral area and severe damage to an important railway bridge. The earthquake was recorded by only one strong-motion station, which presents a challenge for quantitatively estimating the extent of the ground-motion distribution caused by this event. In this study, the spectral element method (SPECFEM3D code), which solves the elastodynamic equations and can capture the full physics of seismic-wave propagation, is employed for broadband (0–10 Hz) ground-motion simulations of this earthquake. A hybrid kinematic source is developed in which the final slip distribution combines a prescribed asperity model based on Interferometric Synthetic Aperture Radar data (as a source for low-frequency radiation) and a stochastic part (as a source for high-frequency radiation), which introduces spatial heterogeneities to the prescribed asperity model. The numerical approach is first validated by modeling the well-recorded 1994 Northridge earthquake before modeling the Menyuan earthquake. The simulated ground motion is compared with the only observed strong-motion station record, as well as with empirical Next Generation Attenuation-West2 ground-motion models. Then, topography effect in Menyuan earthquake is studied in detail. The simulated ground motions with and without surface topography indicate that the topography tends to focus and scatter the seismic wavefield, resulting in amplification of the ground shaking. The results show a significant correlation between the peak ground velocity (PGV) and topography. The PGV amplification caused by topography effects is period dependent, and its peak amplification reaches up to 50% within a typical resonance period (1–2 s). It could be inferred that the railway bridge probably vibrated in resonance and suffered severe damage owing to the amplified long-period ground motion caused by the topography.

The 1886 Charleston, South Carolina, Earthquake: Intensities and Ground Motions

ABSTRACT The 1 September 1886 Charleston, South Carolina, earthquake was one of the largest preinstrumental earthquakes in eastern North America for which extensive contemporaneous observations were documented. The distribution of shaking was mapped shortly after the earthquake, and reconsidered by several authors in the late twentieth century, but has not been reconsidered with a modern appreciation for issues associated with macroseismic data interpretation. Detailed contemporary accounts have also never been used to map the distribution of numerical shaking intensities in the near field. In this study we reconsider macroseismic data from far-field accounts as well as detailed accounts of damage in the near field, estimating modified Mercalli intensity values at 1297 locations including over 200 definite “not felt” reports that delineate the overall felt extent. We compare the results to the suite of ground-motion models for eastern North America selected by the National Seismic Hazard Model, using a recently proposed mainshock rupture model and an average site condition for the locations at which intensities are estimated. The comparison supports the moment magnitude estimate, 7.3, from a recently proposed rupture model (Bilham and Hough, 2023). A ShakeMap constrained by model predictions and estimated intensities further illustrates this consistency, which we show is insensitive to rupture model details. Given the uncertainty of calibration relations for magnitudes close to 7, the overall intensity distribution provides a good characterization of shaking but cannot improve the independent moment magnitude estimate. We also identify a previously unrecognized early large aftershock that occurred 9–10 min after the mainshock, for which we estimate magnitude ∼5.6.

Probabilistic seismic hazard analysis for Bagan (Myanmar)

Following the 2016 Chauk earthquake in Bagan (Myanmar), numerous old pagodas and temples suffered severe damage. This research presents a study on the seismic hazard analysis of the Bagan city in Myanmar, based on a probabilistic framework focussing on analysing 43 temples with their associated local soil information. To this end, two seismic source models are developed based on the tectonic setting of the region and information available. Instrumental and historical records are compiled from both literature and international earthquake catalogues while conducting catalogue completeness. This study uses state-of-the-art ground motion models to perform probabilistic seismic hazard analysis and develop seismic hazard maps for different return periods in the region. Results are also expressed for selected temples in the region in terms of site-specific uniform hazard spectra. The findings indicate significant seismic activity, with peak ground acceleration in the region ranging from 0.25 to 0.36 g for a return period of 2475 years, 0.22–0.32 g for a return period of 975 years, and 0.18–0.24 g for a return period of 475 years. The updated hazard levels indicate that the literature slightly underestimates hazard in the region under study.

Shallow three-dimensional shear wave velocity model for the Mexico City Basin

In this study, a simplified shallow three-dimensional shear wave velocity (Vs) model is presented for the Mexico City Basin. To this end, previous studies were carefully reviewed to assemble a database of Vs and site period measurements for the region. The new site period measurements obtained at the western edge of the Basin were compared to the existing 2004 Complementary Technical Standards of Mexico site period nominal map, (NTC, 2004) the Lermo et al. (2020) site period map, and Design Seismic Actions System (SASID), 2020 site period predictions. Each site period prediction method was shown to have differences with respect to the new measurements along the western edge of the Basin. However, there was no bias for the prediction methods with the Lermo et al. (2020) and SASID predictions, demonstrating less error between the measured values than the NTC (2004) predictions. To develop the three-dimensional Vs model, the shallow (top 60 m) subsurface was divided into five generalized soil layers. The site period was used as the only input for the three-dimensional Vs model to simplify and maximize the model’s applicability. The performance of the model was assessed by comparing the measured and predicted Vs profiles. Overall, the three-dimensional Vs model developed in this study is a valuable tool that can be used along with geophysical estimates of deeper structure for ground motion modeling and preliminary site response studies along with other benefits to the seismic resiliency of the region.