Damped Pseudospectral Acceleration Research Articles

Ground motion models for Fourier amplitude spectra and response spectra using Machine learning techniques

AbstractOne 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.

A relocated earthquake catalog and ground motion database for the southern East African rift system

The southern East African rift system (EARS) is geologically rare considering its early-stage continental rift setting combined with a deep seismogenic zone. Several seismically vulnerable communities are located within this tectonically active region, resulting in a significant seismic risk. However, the ground motion and seismic hazard analyzes necessary to increase the earthquake preparedness in the region have been limited due to the relatively short instrumentation history and scarce ground motion data available. Here, we present a newly compiled ground motion database for the southern EARS which is critically lacking and preventing local ground motion studies. This database includes a regional catalog of 882 earthquakes spanning 1994–2022 (magnitudes 3–6.5) with available waveform records within epicentral distances of 300 km. Three different velocity models were used to relocate 256, 255, and 252 events, respectively, to quantify depth sensitivity, relocating events down to depths of 35–40 km. The final database contains 10,725 time-series records from 353 stations along with P- and S-wave phase arrivals for each record. The ground motion database contains peak ground acceleration and velocity and 5% damped pseudo-spectral acceleration for 291 frequencies from 1.0 to 30 Hz for the horizontal components. In addition, a Fourier amplitude spectrum table for 212 frequencies from 0.1 to 30 Hz is included. The database is accessible through the ISC repository ( https://doi.org/10.31905/4GGVBFBE ).

Stochastic Ground-Motion Simulation of the 2021 Mw 5.9 Woods Point Earthquake: Facilitating Local Probabilistic Seismic Hazard Analysis in Australia

ABSTRACT The 2021 Mw 5.9 Woods Point event is the largest onshore earthquake that has occurred in the recorded history of southeastern Australia since European settlement. To study its source and ground-motion characteristics and to extract information for local seismic hazard analysis, we employ a stochastic finite-fault simulation approach to simulate ground motions for this event based on the observations collected from 36 onshore stations. We determine the regional distance-dependent attenuation parameters using the horizontal Fourier acceleration amplitude spectrum in the frequency range of 0.1–20 Hz. We parameterize path parameters using different models to consider uncertainties and sensitivities. To investigate local site effects, we construct a VS30-based site amplification model. Source parameters are then determined by fitting the theoretical Brune’s ω2 model with a reference Fourier source spectrum at 1.0 km. The κ0 value for the reference rock site is estimated as κ0=0.01 s, and dynamic stress drop is found to be 41.0 MPa by minimizing the overall absolute residual of 5% damped pseudospectral acceleration. We validate the simulations by comparing simulated and observed ground motions in terms of various intensity measurements; analyses of residuals show that the simulations are in good agreement with observations (average residual is close to 0). To facilitate future probabilistic seismic hazard analysis, six selected ground-motion models are ranked using the deviance information criteria based on an independent data set consisting of field observations and simulated ground motions.

A far-field ground motion prediction model for interface earthquakes at the hill zone of Mexico City

This study presents an updated attenuation model to predict the peak ground acceleration (PGA), peak ground velocity (PGV), 5% damped pseudo-spectral acceleration (SA), and the average spectral acceleration (AvgSA) at the hill zone of Mexico City for interface earthquakes. The strong-motion dataset comprises 33 earthquakes recorded at CU station, covering a moment magnitude (Mw) range from 6.0 to 8.1 and a source-to-site distance (Rrup) range from 240 to 490 km. Given the small number of available observations, a Bayesian regression scheme is used to obtain the coefficients of the ground-motion prediction model (GMPM). In addition, the epistemic uncertainty in the estimation of the regression coefficients is evaluated, showing its impact on the framework of a probabilistic seismic hazard analysis (PSHA). The results are compared with models previously developed for the CU station, discussing the differences observed between the median predictions and their standard deviations. Likewise, seismic hazard curves are computed and compared with empirical curves obtained by counting the number of times per year that a given value of ground-motion intensity is exceeded. The results show that the dispersion of the GMPM proposed is lower than the previous models for PGA and SA, which means better predictability and more reliable estimates of the seismic hazard at the site.

Ground motion model for Peninsular India using an artificial neural network

Ground motion models (GMMs) are an essential tool for seismic hazard analysis. They are used for developing predictive relationships to estimate the expected levels of seismic ground shaking through the ground motion parameters (GMPs). There is limited recorded data on the stable continental regions (SCR) such as Peninsular India (PI), and Central and North Eastern America (CENA), and GMMs are developed either by hybrid or stochastic methods. In this study, an effort has been made to develop a GMM for the PI region by compiling the recorded data from the CENA and PI regions, as well as from the Near Source Strong (NESS) data. An artificial neural network coupled with the genetic algorithm is considered to develop GMMs. So, the GMP-1 model is developed for various GMPs for both RotD50 and vertical components, while the GMP-2 model is developed for RotD50 components of peak ground acceleration (PGA), peak ground velocity (PGV), and the 5% damped pseudo-spectral acceleration (PSA) for periods between 0.01 and 3 s. The developed models are valid for magnitudes between 2–7.7 Mw and 0 to 1500 km Joyner–Boore distance. In addition, a parametric study is performed with the developed models for various combinations of predictor variables, and it is noticed that both the models capture the trend and attenuation pattern observed in the recorded data for all the GMPs. Moreover, the GMP-2 model predictions agree well with the recorded data compared with the candidate GMMs developed in the NGA-EAST project. A comparison of the GMP-2 models with the recorded data in the PI region indicates the robustness and effectiveness of the developed model in providing reasonable estimates of GMPs, implying its applicability to tectonic environments similar to the CENA region.

An Improved Probabilistic Seismic Hazard Assessment of Tripura, India

The State of Tripura lies in northeast India which is considered to be one of the most seismically active regions of the world. In the present study, a realistic Probabilistic Seismic Hazard Assessment (PSHA) of Tripura State based on improved seismogenic sources considering layered polygonal sources corresponding to hypo-central depth range of 0 to 25 km, 25 to 70 km and 70 to 180 km, respectively and data driven selection of suitable Ground Motion Prediction Equations (GMPEs) in a logic tree framework is presented. Analyses have been carried out by formulating a layered seismogenic source zonation together with smooth-gridded seismicity. Using the limited accelerogram records available, most suitable GMPEs have been selected after performing a thorough quantitative assessment and thus the uncertainty in selecting appropriate GMPEs in PSHA has been addressed by combining them with proper weight factor. The computations of seismic hazard are carried out in a higher resolution of grid interval of 0.05 $\degree$ X 0.05 $\degree$. The probabilistic seismic hazard distribution in terms of Peak Ground Acceleration (PGA) and 5% damped Pseudo Spectral Acceleration (PSA) at different time periods for 10% and 2% probability of exceedance in 50 years at engineering bedrock level have been presented. The final results show significant improvements over the previous studies, which is reflecetd in the local variation of the hazard maps. The design response spectra at engineering bedrock level can be computed for any location in the study region from the hazard distributions. The results will be useful for earthquake resistant design and construction of structures in this region.

A New Set of Automated Methodologies for Estimating Site Fundamental Frequency and Its Uncertainty Using Horizontal-to-Vertical Spectral Ratio Curves

AbstractThis study proposes a new set of methodologies to estimate the site fundamental frequency using the horizontal-to-vertical spectral ratio (HVSR) of recorded surface ground motions. Because of the lack of consensus in HVSR calculation among researchers, a wide range of methods are practiced in this area, yielding different site fundamental frequencies at a given site due to analyst subjectivity. In this study, current practices for combining horizontal components—geometric mean and RotD50—are examined first, and results show that both methods provide comparable HVSR curves. However, RotD50 has the advantage of being orientation independent. Second, the application of Fourier amplitude spectrum (FAS) and 5% damped pseudospectral acceleration (PSA) in computing HVSR is studied, and results are presented for one case study in which PSA-based HVSR seems to suffer from scenario dependency, whereas the FAS-based results appear stable. Different values for Konno–Ohmachi smoothing parameter b were evaluated, and its effect on estimating the site fundamental frequency was considered; using b = 40 is our preference. In previous studies, average HVSR curves were used to estimate a single number representing the site fundamental frequency without incorporating uncertainty. This study proposes four methodologies that use events’ individual HVSR curves to estimate the site fundamental frequency and its associated uncertainty in a completely automated manner. Methods 1–3 use individual HVSR curves to find the maximum-likelihood estimate of the site fundamental frequency (fml), whereas method 4 uses both individual and average HVSR curves to estimate the first resonance frequency (f0). To evaluate the automated methods, a subset of the Next Generation Attenuation-West2 dataset is used to study 50 stations, and the results are compared with an independent study demonstrating good consistency. The proposed methods are further illustrated using data from the Garner Valley Downhole Array (GVDA), which highlights the pros and cons of the presented methods.

Machine-Learning-Based Surface Ground-Motion Prediction Models for South Korea with Low-to-Moderate Seismicity

ABSTRACT Ground-motion prediction models (GMPMs) have been developed to estimate seismic intensity considering earthquake magnitude, source-to-site distance, site condition, and so on. This study proposes GMPMs to predict 5% damped pseudospectral acceleration (PSA) for 27 periods ranging from 0.01 to 10 s in Korea, based on three machine-learning techniques (i.e., artificial neural network [ANN], random forest [RF], and gradient boosting [GB]). We use 1189 ground motions recorded at 50 surface stations during the 77 earthquakes with a local magnitude (ML) greater than 3.0, including the Gyeongju and Pohang earthquakes with ML of 5.8 and 5.4, respectively. We compare the performances of the three machine-learning-based models and the classical regression-based model in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), standard deviation of residuals, and between-event and within-event residuals. The GB-based model shows the best performance. In addition, we analyze the working process of the GB-based model using variable importance and partial dependence (PD) plots. Among the five independent variables (ML, epicentral distance [Repi], average shear-wave velocity of the upper 30 m [VS30], focal depth, and slope angle) used in this study, ML and Repi are the most influential variables and show strong correlations with PSAs. We apply the GB-based model to three recent earthquakes larger than ML 3.0, and the model accurately predicts the PSAs at various stations. We also generate maps of estimated PSA (PSAest) values for the four periods (T = 0.01, 0.1, 1, and 3 s) for the scenario earthquake with an ML of 5.0. We provide a method for training the GB-based model using the Python library, which can enhance the ground-motion prediction not only in Korea but also worldwide, and an executable version of the validated GB-based model.

PEER NGA-East database

This article documents the earthquake ground motion database developed for the NGA-East Project, initiated as part of the Next Generation Attenuation (NGA) research program and led by the Pacific Earthquake Engineering Research Center (PEER). The project was focused on developing a ground motion characterization model (GMC) model for horizontal ground motions for the large region referred to as Central and Eastern North America (CENA). The CENA region covers most of the U.S. and Canada, from the Rocky Mountains to the Atlantic Ocean and is characterized tectonically as a stable continental region (SCR). The ground-motion database includes the two- and three-component ground-motion recordings from numerous selected events relevant to CENA ( M > 2.5, with distances up to 3500 km) that have been recorded since 1976. The final database contains over 27,000 time series from 82 earthquakes and 1271 recording stations. The ground motion database includes uniformly processed time series, 5% damped pseudo-spectral acceleration (PSA) median-component ordinates for 429 periods ranging from 0.01 to 10 s, duration and Arias intensity in 5% increments, and Fourier amplitude spectra for different time windows. Ground motions and metadata for source, path, and site conditions were subjected to quality checks by topical working groups and the ground-motion model (GMM) developers. The NGA-East database constitutes the largest database of processed recorded ground motions in SRCs and is publicly available from the PEER ground-motion database website.

Ground motion prediction equation for crustal earthquakes in Taiwan

We develop a ground motion prediction equation (GMPE) for estimating horizontal ground motion amplitudes caused by crustal earthquakes, based on an integrated data set that includes strong motion recordings mainly from Taiwan earthquakes and only from large magnitude earthquakes in the NGA-West2 database. This GMPE is developed for probabilistic seismic hazard analysis study, which is introduced as a part of Taiwan Senior Seismic Hazard Analysis Committee Level 3 projects. The functional form developed by Chiou and Youngs was carefully studied to determine the key modeling parameters needed to regress against ground motion in the target region. Using this functional form, the GMPE achieves considerable improvement over previously developed Taiwan GMPEs. In particular, the use of a high-order function in magnitude scaling enables representation of the saturation effects of large earthquakes. Moreover, consideration of focal mechanisms, depth effects, and dip effects are used to correct the magnitude scaling; consideration of nonlinear site amplification is conditioned on VS30and reference ground motion on rock; and consideration of basin depth effect is a function of Z1.0in correlation with VS30. In addition, ground motion data used in this study are not only expanded by more than three times as many earthquakes and records compared with a previous Taiwan model but also provide the metadata of these records that were not available or were previously incomplete. In this study, we compare the proposed model with the NGA-West2 models and discuss the regional difference in ground motion in terms of spectral shape, magnitude scaling, distance scaling, depth scaling, style of faulting, and site effects. We provide median and single standard deviations of peak ground acceleration and 5% damped pseudospectral acceleration response ordinates of the orientation-independent average horizontal component of ground motion (RotD50) for the spectral period of 0.01–10 s.

Ground motion prediction equation for Taiwan subduction zone earthquakes

A ground motion prediction equation (GMPE) is presented for computing the median and standard deviation of peak ground acceleration (PGA) and 5% damped pseudo-spectral acceleration (PSA) for periods between 0.01 s and 5.0 s for probabilistic seismic hazard analysis (PSHA) and engineering applications in Taiwan. An integrated strong motion dataset consisting of two subduction earthquake regions was selected from 3314 recordings from Taiwan with M4.5 to M7.1 and 3376 recordings from Japan with M6.5 to M9.1. This dataset was then used to validate, and refit where necessary, the function form provided by Abrahamson et al. for application to Taiwan subduction earthquakes. The proposed model accounts for the extrapolation behaviors associated with the large-magnitude scaling and the near-source scaling terms, both of which were developed empirically by using the combined Taiwan–Japan dataset. The distance attenuation and site term were developed specifically for the Taiwan region. The site term is based on two parameters; the time-averaged shear wave velocity of the top 30 m depth ( VS30) and the depth-to-the-shear wave velocity horizon of 1.0 km/s ( Z1.0).

Evaluation of Ground‐Motion Models for U.S. Geological Survey Seismic Hazard Models: 2018 Anchorage, Alaska, Mw 7.1 Subduction Zone Earthquake Sequence

Abstract Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska (Mw 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

Earthquake hazard potential of Indo-Gangetic Foredeep: its seismotectonism, hazard, and damage modeling for the cities of Patna, Lucknow, and Varanasi

The Indo-Gangetic Foredeep region lies in close proximity to the Himalayan collision tectonics and the Peninsular Shield thereby subjecting it to repeated strong ground shaking from large and great earthquakes from these active tectonic regimes. An attempt is, therefore, made to understand the seismotectonic regime of the Indo-Gangetic Foredeep region while performing probabilistic seismic hazard modeling of its important cities of Patna and Lucknow and the religious city of Varanasi based on consideration of seismogenic source characteristics, smoothened gridded seismicity zoning, and generation of next generation ground motion attenuation models appropriate for this region along with other existing region-specific ground motion prediction equations in a logic tree framework. In the hazard modeling, peak ground acceleration (PGA) and 5% damped pseudo-spectral acceleration (PSA) at different time periods for 10 and 2% probability of exceedance in 50 years with a return period of 475 and 2475 years have been estimated at firm rock site condition (site class B/C) with an average shear-wave velocity of about 760 m/s, of which, however, the results of only 475 years of return period have been presented here for urban development and earthquake engineering point of view. Surface-consistent probabilistic seismic hazard is modeled using the International Building Code-compliant short and long period site factors corresponding to topographic gradient-derived shear-wave velocity-based site classes. The estimated surface-consistent PGA is seen to vary in the range of 0.222–0.238 g in Patna City, while it varies in the range 0.257–0.295 g in Lucknow and 0.146–0.172 g in the city of Varanasi. The cumulative damage probabilities in terms of ‘none,’ ‘slight,’ ‘moderate,’ ‘extensive,’ and ‘complete’ have been assessed using the capacity spectrum method in the Seismic Loss Estimation Approach (SELENA) using both the fragility and capacity functions for six model building types in these cities. The discrete damage probability exhibits that the building types ‘IGW-RCF2IL (PAGER/FEMA:C1L),’ ‘IGW-RCF21M (PAGER/FEMA:C1M),’ and ‘IGW-RCF11L (PAGER/FEMA:C3L)’ will suffer minimum damage, while ‘IGW-RCF21H (PAGER/FEMA:C1H),’ ‘IGW-RCF11M (PAGER/FEMA:C3M),’ and ‘PAGER/FEMA:C3H’ will suffer extensive damage in the event of a maximum earthquake of Mw 7.2 impacting the terrain as predicted from median nodal maximum magnitudes in a heuristic search in the probabilistic protocol. The estimated probabilistic seismic hazard and damage scenario are expected to play vital roles in the earthquake-inflicted disaster mitigation and management of these cities for better predisaster prevention, preparedness, and postdisaster rescue, relief, and rehabilitation. The results may also be incorporated in the building codal provisions of these smart cities as intended by the Federal Government of India.

Ground motion prediction equations for distant subduction interface earthquakes based on empirical data in the Malay Peninsula and Japan

Ground motion prediction equations (GMPEs) for earthquakes that occur in subduction zones, including both interface and slab parts, have a major impact on seismic hazard analysis in many parts of the world. For example, in the Sumatran subduction region, there could be a remarkable hazard for the Malay Peninsula due to the large megathrust earthquakes that occur far from the region along the subduction interface. This study has developed new empirical spectral GMPEs for long-distance subduction interface earthquakes based on the recorded ground motion data in the Malay Peninsula and Japan. The compiled ground motion database is from hundreds of ground motion recordings due to twenty five reliably identified subduction interface events with moment magnitude [M] of 5.0–9.1 and hypocentral distance (Rhyp) up to 1300 km. The data from the large megathrust earthquakes with M ≥ 7.0 such as 2011 M 9.1 Tohoku-Japan, the 2007 M 8.5 earthquake near Bengkulu in Sumatra Island, 2005 M 8.6 Nias-Sumatra, and 2004 M 9.0 Aceh-Sumatra earthquakes were included in the database. The proposed GMPEs are able to predict peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped pseudo-spectral acceleration (PSA) for four different site classes based on the National Earthquake Hazards Reduction Program (NEHRP) site classification. The results of this study could be applied to develop logic tree frameworks for seismic hazard analyses of Peninsular Malaysia as well as the regions affected by large and distant subduction interface earthquake events.

A new approach to site classification: Mixed-effects Ground Motion Prediction Equation with spectral clustering of site amplification functions

With increasing amount of strong motion data, Ground Motion Prediction Equation (GMPE) developers are able to quantify empirical site amplification functions (ΔS2Ss) from GMPE residuals, for use in site-specific Probabilistic Seismic Hazard Assessment. In this study, we first derive a GMPE for 5% damped Pseudo Spectral Acceleration (g) of Active Shallow Crustal earthquakes in Japan with 3.4≤Mw≤7.3 and 0≤RJB<600km. Using k-mean spectral clustering technique, we then classify our estimated ΔS2Ss (T=0.01–2 s) of 588 well-characterized sites, into 8 site clusters with distinct mean site amplification functions, and within-cluster site-to-site variability ~ 50% smaller than the overall dataset variability (ϕS2S). Following an evaluation of existing schemes, we propose a revised data-driven site classification characterized by kernel density distributions of Vs30, Vs10, H800, and predominant period (TG) of the site clusters

Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data

Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude (Mw), the Joyner–Boore distance (R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057–1085, 2014; Derras et al., in Earthquake Spectra 32:2027–2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated.

Erratum to: A model for predicting vertical component peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudospectral acceleration (PSA) for Europe and the Middle East

In this study, we present a ground-motion model for the vertical component of peak ground acceleration, peak ground velocity, and 5% damped pseudo acceleration response spectra at periods ranging from 0.01 to 4 s. The vertical model is based on the ground-motion models previously developed for the horizontal component and vertical-to-horizontal ratio of ground motion by Akkar et al. (Bull Earthq Eng 12:359–387, 2014a; 517–547, 2014b) rather than on an independent regression analysis of strong-motion data available for Europe and the Middle East. The proposed ground-motion model includes formulations for the median values as well as for the aleatory within-event, between-event, and total standard deviation values of the vertical ground motion. We validate the proposed model by comparing it against the strong-motion database of Europe and the Middle East. Our vertical ground-motion model is applicable for moment magnitudes ranging from 4.0 to 8.0, for source-to-site distances ranging from 0 to 200 km, average shear-velocity down to 30 m (Vs30) values ranging from 150 to 1200 m/s and for reverse, normal and strike-slip styles of faulting as is the case for the underlying horizontal component and vertical-to-horizontal ratio ground-motion models of Akkar et al. (2014a, b). Within the scope of this study, a method to develop a vertical spectrum that is fully consistent with the corresponding horizontal uniform hazard spectrum is also proposed.

Alternative (G‐16v2) Ground‐Motion Prediction Equations for Central and Eastern North America

This article introduces new ground‐motion prediction equations (GMPE) for central and eastern North America that represents an alternative, more physically justified approach to ground‐motion attenuation modeling than my previous Graizer (2016) G‐16 model. The new model has a bilinear slope of ∼ R −1 within 70 km from the fault (confirmed by empirical data) with a slope of ∼ R −0.5 at larger distances corresponding to the geometrical spreading of body and surface waves. This new (G‐16v2) model is based on the Next Generation Attenuation (NGA)‐East database for the horizontal peak ground acceleration and 5% damped pseudospectral acceleration RotD50 component (Goulet et al. , 2014) and also on comparisons with western United States data and ground‐motion simulations. Based on data, I estimated the average slope of the distance attenuation within the 50–70 km distance from the fault to be ∼−1.0 at most of the frequencies supporting regular geometrical spreading of body waves. Multiple inversions are performed to estimate apparent (combination of intrinsic and scattering) attenuation of response spectral acceleration (SA) amplitudes from the NGA‐East database for incorporation into the GMPE. These estimates demonstrate a difference between classical seismological Q ( f ) and the above‐mentioned attenuation factor that I recommend calling Q SA( f ). Based on residuals, I adjusted previously developed site correction (Graizer, 2016) that was based on multiple runs of representative V S 30 (time‐averaged shear‐wave velocity in the upper 30 m of the geological material) profiles through SHAKE‐type equivalent‐linear codes. Site amplifications are calculated relative to the hard‐rock definition used in nuclear industry ( V S =2800 m/s). These improvements resulted in a modest reduction in total ( σ ) and within‐event (![Graphic][1] ) logarithmic standard deviations in the new G‐16v2 relative to G‐16 model. The number of model predictors is limited to a few measurable parameters: moment magnitude M , closest distance to fault rupture plane R rup, V S 30, and apparent attenuation factor Q SA( f ). The model is applicable for the stable continental regions and covers the following range: 4.0≤ M ≤8.5, 0≤ R rup≤1000 km, 450≤ V S 30≤2800 m/s, and frequencies 0.1≤ f ≤100 Hz. [1]: /embed/inline-graphic-1.gif

A model for predicting vertical component peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped pseudospectral acceleration (PSA) for Europe and the Middle East

A model for predicting vertical component peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped pseudospectral acceleration (PSA) for Europe and the Middle East

A model for estimating horizontal aftershock ground motions for active crustal regions

Inconsistent observations on the characteristics of ground motions from aftershocks have been found, while there is increasing attention to effects of aftershock ground motions on structural behaviors. This study examines differences in ground motions from main shock and aftershock earthquakes using the NGA-West2 database, and propose an empirical model to estimate the average horizontal components of peak ground acceleration (PGA), and peak ground velocity (PGV), and 5% damped pseudo spectral acceleration (PSA) at various spectral periods of aftershock earthquakes for tectonically active crustal regions, as a function of aftershock to main shock magnitude ratio, distance ratio, and time-averaged shear-wave velocity in the upper 30m of soil deposits (VS30). Spectral accelerations from aftershock earthquakes are smaller than those from main shock earthquakes given the same magnitude, especially at short periods. Performance of the proposed model is evaluated using a mixed-effects residuals analysis. Period-dependent standard deviation of residuals is also presented.