Ground Motion Model Research Articles

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.

Seismic Hazard Curve as Dynamic Parameters in Earthquake Building Design for Sabah, Malaysia

This paper presents the significance of a seismic hazard curve plot as a dynamic parameter in estimating earthquake-resistant structures. Various cases of structural damages in Malaysia are due to underestimating earthquake loadings since mostly buildings were designed without seismic loads. Sabah is classified as having low to moderate seismic activity due to a few active fault lines. Background point, area, and line sources are the three tectonic features that have impacted Sabah. Data on earthquakes from 1900 to 2021 have been collected by a number of earthquake data centers. The seismicity is based on a list of historical seismicities in the area, which stretches from latitudes 4 °S to 8 °N and longitudes 115 °E to 120 °E. The goal of this research is to develop a seismic hazard curve based on a conventional probabilistic seismic hazard analysis being examined for the maximum peak ground acceleration at 10% probability of exceedance as published in MSEN1998-1:2015. This study extended to 5% and 2% probability of exceedance combined with the seismic hazard curve by using Ranau as a case study. To calculate the expected ground motion recurrence, such as peak ground acceleration at the site, earthquake recurrence models were combined with selected ground motion models. A logic tree structure was used to combine simple quantities such as maximum magnitudes and the chosen ground motion models to describe epistemic uncertainty. The result demonstrates that peak ground acceleration values at the bedrock were estimated to be 0.16, 0.21, and 0.28 g of the total seismic hazard curve at 10%, 5%, and 2% PE in a 50-year return period, respectively. The seismic hazard study at a Ranau site basically depends on the seismicity of a region and the consequences of failure in the past. Thus, the results can be used as a basis for benchmarking design or evaluation decisions and for designing remedial measures for Sabah constructions to minimize structural failure.

U.S. National VS30 Models and Maps Informed by Remote Sensing and Machine Learning

Abstract The shear-wave velocity time averaged over the upper 30 m (VS30) is widely used as a proxy for site effects, forms the basis of seismic site class, and underpins site-amplification factors in empirical ground-motion models. Many earthquake simulations, therefore, require VS30. This presents a challenge at regional scale, given the infeasibility of subsurface testing over vast areas. Although various models for predicting VS30 have thus been proposed, the most popular U.S. national, or “background,” model is a regression equation based on just one variable. Given the growth of community data sets, satellite remote sensing, and algorithmic learning, more advanced and accurate solutions may be possible. Toward that end, we develop national VS30 models and maps using field data from over 7000 sites and machine learning (ML), wherein up to 17 geospatial parameters are used to predict subsurface conditions (i.e., VS30). Of the two models developed, that using geologic data performs marginally better, yet such data are not always available. Both models significantly outperform existing solutions in unbiased testing and are used to create new VS30 maps at ∼220 m resolution. These maps are updated in the vicinity of field measurements using regression kriging and cover the 50 U.S. states and Puerto Rico. Ultimately, and like any model, performance cannot be known where data is sparse. In this regard, alternative maps that use other models are proposed for steep slopes. More broadly, this study demonstrates the utility of ML for inferring below-ground conditions from geospatial data, a technique that could be applied to other data and objectives.

Seismic fragility analysis using stochastic polynomial chaos expansions

Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand parameter (EDP) exceeds a certain safety threshold given a set of selected intensity measures (IMs) that characterize the earthquake load. The-state-of-the art methods for fragility computation rely on full non-linear time–history analyses. Within this perimeter, there are two main approaches: the first relies on the selection and scaling of recorded ground motions; the second, based on random vibration theory, characterizes the seismic input with a parametric stochastic ground motion model (SGMM). The latter case has the great advantage that the problem of seismic risk analysis is framed as a forward uncertainty quantification problem. However, running classical full-scale Monte Carlo simulations is intractable because of the prohibitive computational cost of typical finite element models. Therefore, it is of great interest to define fragility models that link an EDP of interest with the SGMM parameters — which are regarded as IMs in this context. The computation of such fragility models is a challenge on its own and, despite a few recent studies, there is still an important research gap in this domain. This comes with no surprise as classical surrogate modeling techniques cannot be applied due to the stochastic nature of SGMM. This study tackles this computational challenge by using stochastic polynomial chaos expansions to represent the statistical dependence of EDP on IMs. More precisely, this surrogate model estimates the full conditional probability distribution of EDP conditioned on IMs. We compare the proposed approach with some state-of-the-art methods in two case studies. The numerical results show that the new method prevails over its competitors in estimating both the conditional distribution and the fragility functions.

Ground-Motion Model (GMM) for Crustal Earthquakes in Northern South America (NoSAm Crustal GMM)

ABSTRACT Crustal earthquakes are some of the main contributors to the seismic hazard in northern South America (NoSAm). There is evidence of historical crustal events with epicenters near populated cities, such as the 1999 Mw 6.2 Coffee Region earthquake, whose damages added up to 1.9% of Colombia’s gross domestic product and reported about 1200 deaths. Because the global crustal ground-motion models (GMMs) routinely used in seismic hazard assessments of the region are biased with respect to the available ground-motion records, this article presents a regional GMM developed using local data from earthquakes in Colombia, Ecuador, and Venezuela. The filtered database contains 709 triaxial records from 56 earthquakes, recorded at 92 stations between 1994 and 2020 by the Colombian Geological Survey. The moment magnitudes of the events range between 4.5 and 6.8, with hypocentral depths ≤60 km. The model covers rupture distances ≤350 km. The model site amplification is based on a categorization approach relying on the predominant site period, identified through the horizontal-to-vertical response ratios of 5%-damped response spectra. The proposed GMM is developed as a regionalization of the global Next Generation Attenuation-West2 Project ASK14 model. Our model corrects the misfit of the ASK14 GMM with respect to the observed ground-motion data in NoSAm for moderate magnitudes and intermediate to large distances while keeping the extrapolation capabilities. The proposed GMM considers the added attenuation for ray paths crossing the volcanic arc. Analysis of the variance components allows approximating plausible reductions of the standard deviation in future nonergodic models.

Reducing the uncertainties in the NGA-West2 ground motion models by incorporating the frequency and amplitude of the fundamental peak of the horizontal-to-vertical spectral ratio of surface ground motions

Ground motion models (GMMs) are developed empirically to predict ground motion intensity measures. The site effects in GMMs are usually addressed using time-averaged shear-wave velocity for the upper 30 m of the site (VS30) and shear-wave isosurface depth (i.e. Z1.0 and Z2.5). However, the former does not include the effect of layered soil structure on the site’s response, and the latter is generally inferred. Many studies have shown that site fundamental frequency can be used as an additional site proxy. Using horizontal-to-vertical spectral ratio (HVSR), one can estimate the site fundamental frequency in a fast and inexpensive way. In this article, a model is developed to incorporate site fundamental frequency and its corresponding amplification factor in the Next Generation Attenuation (NGA)-West2 GMMs to reduce the uncertainties. First, a subset of the NGA-West2 database consisting of 14,636 recordings from 298 events is selected after a two-step screening procedure and used to compute the horizontal-to-vertical response spectral ratio (HVSRPSA) of recorded surface ground motions. Second, automated methodologies previously developed by the authors are used to obtain maximum likelihood estimates of site fundamental frequency (fml), its corresponding amplitude (Aml), and associated uncertainties. Third, a mixed-effect maximum likelihood approach is implemented for residual analysis and site-term residual calculation. Finally, an HVSR-based model is developed for the NGA-West2 dataset considering the relationship between site-term residual and the HVSR-based proxies (i.e. fml and Aml). In this model, Aml is as important as fml in reducing the uncertainties. Results show that using a single HVSR-based model can consistently reduce the site-to-site variability ([Formula: see text]) for all NGA-West2 GMMs, which already include the effect of VS30, Z1.0, or Z2.5. Besides, the reduction in [Formula: see text] is period-dependent, with an average of 13% reduction. As a result of the reduction in [Formula: see text], total standard deviation ( σ) decreases by 3.5% on average.

Linearly combined ground motion model using quadratic programming for low- to mid-size seismicity region: South Korea

This study suggests a linearly combined ground motion model (GMM) optimized for the within-rock conditions of South Korea. Ground motions recorded by accelerometers positioned in within-rock layers are used as target intensity measures (IMs), and five local GMMs and four global GMMs are used as candidates for combination. Optimization, which seeks to find a weight vector minimizing the uncertainty of the combined model, is performed using the quadratic programming (QP) technique, which provides very fast and solid results for the linear combination problem. This study illustrates how to use the QP technique for the linear combination problems. Also, we suggest optimized weight vectors for GMM combinations for two conditions: 1) the IM prediction of a scenario event without observations and 2) the IM prediction of a past event with observations. Among the local and global GMMs considered, JB03, Eea15, JH21, and BSSA14 are selected as the best four GMMs for the first condition, and Jea02, JB03, JH21, and CB14 are selected as the best four GMMs for the second condition. The combined model reduces the standard deviation of residuals in natural logarithms by 10% and 8% for the first and second conditions, respectively, compared to the best individual GMM at each period. Among the GMMs considered, the prediction by Eea15 is only applicable for magnitudes less than 5. Hence, for large magnitude (Mw > 5) prediction, CB14 is recommended instead of Eea15 for the best four models for the first condition.

Development of a New Ground Motion Model for a Peninsular Indian Rock Site

The ground motion model (GMM) plays a vital role in the generation of seismic design basis ground motion parameters. Even though many intra-plate GMMs are available, very few of them are based on Peninsular India (PI) region-specific seismological parameters. Hence, it is imperative to develop a GMM using seismological parameters derived from earthquakes in the Peninsular Indian region. In this study, a new GMM is developed for a PI rock site. Due to the scarcity of real earthquakes, artificial earthquake records are simulated to generate a new GMM for PI. The accelerograms of these artificial earthquakes are obtained from the stochastic finite fault simulation technique. Region-specific seismological parameters are obtained from the available PI earthquakes. The generated GMM is compared with other intra-plate GMMs for different earthquake magnitudes. Also, the generated GMM is validated with the Koyna earthquake record and it is observed that the GMM’s predictions are closer to the record.

Парамуширское землетрясение 25 марта 2020 г. Mw = 7.4

The strong Mw = 7.4 earthquake occurred on March 25, 2020 in the region of Northern Kuril Islands, with its epicenter on the ocean side of the Kuril-Kamchatka deep-water trench, to the east from its axis. The earthquake was felt on all the Kuril Islands, South and East Kamchatka, the maximum shaking was recorded in Severo-Kurilsk with intensity I = VI–VII. Distinct tsunami wave was also registered. In the article, this earthquake and its tectonic position are discussed in the context of the seismicity of the Kuril-Kamchatka arc. The actions of duty shifts in earthquake processing are described, and a detailed description of macroseismic effects is given. The results of the analysis of peak ground motion amplitudes, focal mechanisms and models of the source, tsunami propagation features are also shown. Coseismic displacements revealed by GNSS observation data are presented and compared with model data. Peculiarities and stages of the aftershock process are discussed and the size of the source area is estimated.

Nonlinear Stochastic Seismic Response Analysis of Three-Dimensional Reinforced Concrete Piles

A reliable assessment and design of engineering structures requires appropriate estimation and consideration of different sources of uncertainty. The randomness of seismic ground motion is one major uncertainty that needs to be considered from the perspective of performance-based earthquake engineering. To properly account for this uncertainty and its corresponding effect on pile foundations, a stochastic seismic response analytical framework based on a probability density evolution method and a stochastic ground motion model is proposed for the nonlinear stochastic seismic response analysis of pile foundations. A three-dimensional finite element model of a reinforced concrete pile with a large diameter embedded in the soil foundation is firstly established and calibrated using the full-scale lateral load test results from the literature, in which the nonlinear behavior of soil, the soil–pile interaction, the concrete damaged plasticity, and the steel yielding of the pile are properly modeled. Then, the calibrated three-dimensional numerical model is employed as an illustrative example for the stochastic seismic response analysis using the proposed framework. The mean, standard deviation, and probability density function of pile settlement and the dynamic reliabilities of the pile concerning various performance requirements are obtained in the present study. The settlements of the pile show great variability under the excitation of various stochastic ground motions, and the maximum mean value of the pile settlements is about 8 mm. The changing shape of the settlement probability density functions in every moment demonstrates that it is unreasonable to use assumed probabilistic distribution models to characterize the seismic responses of pile foundations during a seismic reliability analysis. Based on the proposed method, it is found that the dynamic reliabilities of the selected reinforced concrete pile concerning four different performance levels are 0, 0.1293, 0.8247, and 1, respectively. The proposed stochastic seismic response analytical method in the present study can provide a proper and comprehensive way to quantify various uncertainties and their corresponding effects on the seismic performance of pile foundations. It can also be used to estimate the actual reliability level of pile foundations that are designed by the current codes when they are subjected to seismic loads.

Stochastic finite fault modeling of Mw 6.4 earthquake along Indo-Burmese arc and site classification of its seismic recording station

This study simulates the strong ground motion of an earthquake having magnitude 6.4 occurred on 4 February 2011 in Indo-Burmese Arc (IBA). This earthquake recorded on seven seismic recording stations namely Jowai, Jorhat, Sibsagar, Nagaon, Guwahati, Cooch Bihar and Kokhrajhar. Also, estimation of site class performed to observe the site effects of recording stations in this study. The simulation performed on the basis of revised stochastic finite fault model with dynamic corner frequency and site class for the seismic recording station where the earthquake recorded has been estimated by using Horizontal to Vertical Spectral Ratio (HVSR) technique because modeling and simulation of ground motion majorly depend upon the recorded ground motion as well as site effects, where earthquake has been recorded. The revised well known finite source simulation program (EXSIM) used in this study becomes efficient in providing the results for small magnitude earthquake occurred at large distance. The average HVSR curves plotted for all the recording stations, providing peak frequency for all 7 sites. It is observed from the present work that simulated time histories as far as primary attributes, for example, Fourier spectra, Peak ground acceleration (PGA), Response spectra, Predominant frequency are in close concurrence with the recorded ones. The predominant frequency (fpeak) obtained from HVSR technique has been validated from existing literature for the estimation of site class for seismic recording stations. The fpeak determines the average shear wave velocity (Vs30) at depth of 30 ​m of overburden thickness which varies from 200 ​m/s to 1600 ​m/s according to Program for Excellence in Strong Motion Studies (PESMOS). Whereas, National Earthquake Hazard Reduction Program (NEHRP) provision consider the site class for Vs30 below 180 ​m/s. Due its wide range of site characterization, it has been adopted globally. Therefore, in this study, the site class obtained has been compared with the site classification scheme recommended by PESMOS, NEHRP provisions and previous literature. However, the ground motion parameters and the soil classification of the earthquake recording stations for the IBA region has not been explored yet even after seeing its severity in the past decades. Productive representation of present earthquake gives the confidence to understand and evaluate seismic danger at various parts of Indo- Burmese region from tremors of various sizes. In this study it is proposed that PESMOS site classification data should be revised because it covers wide range of soil types just into three (A, B and C type) class.

A database of ground motion recordings, site profiles, and amplification factors from the Groningen gas field in the Netherlands

A comprehensive database that has been used to develop ground motion models for induced earthquakes in the Groningen gas field is provided in a freely accessible online repository. The database includes more than 8500 processed ground motion recordings from 87 earthquakes of local magnitude ML between 1.8 and 3.6, obtained from a large network of surface accelerographs and borehole geophones placed at 50 m depth intervals to a depth of 200 m. The 5%-damped pseudo-acceleration spectra and Fourier amplitude spectra of the records are also provided. Measured shear-wave velocity (VS) profiles, obtained primarily from seismic Cone Penetration Tests (CPTs), are provided for 80 of the ∼100 recording stations. A model representing the regional dynamic soil properties is presented for the entire gas field plus a 5 km onshore buffer zone, specifying lithology, VS, and damping for all layers above the reference baserock horizon located at about 800 m depth. Transfer functions and frequency-dependent amplification factors from the reference rock horizon to the surface for the locations of the recording stations are also included. The database provides a valuable resource for further refinement of induced seismic hazard and risk modeling in Groningen as well as for generic research in site response of thick, soft soil deposits and the characteristics of ground motions from small-magnitude, shallow-focus induced earthquakes.

Regional physics-based simulation of ground motion within the Rhȏne Valley, France, during the MW 4.9 2019 Le Teil earthquake

In this paper we introduce the 3D physics-based numerical simulations (PBS) of ground motion during the Nov 11, 2019, MW 4.9 Le Teil earthquake, which occurred in a low-to-moderate seismicity area in the South-East of France, within the Rhône river valley, which hosts several operating nuclear installations. The numerical code SPEED, developed at Politecnico di Milano, Italy, was used to produce the PBS. After introducing the criteria to construct the numerical model, based on the relatively limited data available, a numerical convergence test was made to identify the frequency range for accurate simulations. Furthermore, the performance of the numerical results against the available strong motion records was assessed quantitatively using Goodness-of-fit (GoF) measures. According to the GoF scores, a good-to-excellent agreement was found on the horizontal components up to 8 Hz, showing that, even without a very detailed 3D numerical model of the medium, that would imply detailed knowledge of the basin shape, of the bedrock-to-basin impedance ratio, as well as of the damping ratio in the basin and its dependence on frequency, the PBS may provide realistic broadband predictions of earthquake ground motion. Nevertheless, as shown by the poorer performance on the vertical component, the high-frequency limitations of PBS, also in relation to the energy radiated by the kinematic source model, is still an issue to be carefully addressed. In spite of these limitations, the results obtained in this work demonstrate that PBS, if suitably calibrated and validated, can be either an alternative or a useful complement to empirical ground motion models, especially in those cases where the region- and site-specific features of ground shaking, including near-source conditions, are typically not accounted for by ergodic empirical models, such as for the seismic risk evaluation of large urban areas and/or of strategic structures, infrastructures and industrial plants.

Conditional ground-motion model based on RVT spectral moments for converting Fourier amplitude spectra to response spectra

Conditional ground-motion model based on RVT spectral moments for converting Fourier amplitude spectra to response spectra

Validation of earthquake ground-motion models in southern California, USA, using precariously balanced rocks

Accurate estimates of earthquake ground shaking rely on uncertain ground-motion models derived from limited instrumental recordings of historical earthquakes. A critical issue is that there is currently no method to empirically validate the resultant ground-motion estimates of these models at the timescale of rare, large earthquakes; this lack of validation causes great uncertainty in ground-motion estimates. Here, we address this issue and validate ground-motion estimates for southern California utilizing the unexceeded ground motions recorded by 20 precariously balanced rocks. We used cosmogenic 10Be exposure dating to model the age of the precariously balanced rocks, which ranged from ca. 1 ka to ca. 50 ka, and calculated their probability of toppling at different ground-motion levels. With this rock data, we then validated the earthquake ground motions estimated by the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3) seismic-source characterization and the Next Generation Attenuation (NGA)-West2 ground-motion models. We found that no ground-motion model estimated levels of earthquake ground shaking consistent with the observed continued existence of all 20 precariously balanced rocks. The ground-motion model I14 estimated ground-motion levels that were inconsistent with the most rocks; therefore, I14 was invalidated and removed. At a 2475 year mean return period, the removal of this invalid ground-motion model resulted in a 2−7% reduction in the mean and a 10−36% reduction in the 5th−95th fractile uncertainty of the ground-motion estimates. Our findings demonstrate the value of empirical data from precariously balanced rocks as a validation tool for removing invalid ground-motion models and, in turn, reducing the uncertainty in earthquake ground-motion estimates.

A Real-Time and Data-Driven Ground-Motion Prediction Framework for Earthquake Early Warning

ABSTRACTThe ShakeAlert earthquake early warning system in the western United States characterizes earthquake source locations and magnitudes in real time, issuing public alerts for areas where predicted ground-motion intensities exceed a threshold value. Although rapid source characterization methods have attracted significant scientific attention in recent years, the ground-motion models used by ShakeAlert have received notably less. This study develops a data-driven framework for earthquake early warning-specific ground-motion models by precomputing and incorporating site-specific corrections, while using a Bayesian approach to estimate event-specific corrections in real time. The study involves analyzing a quality-controlled set of more than 420,000 seismic recordings from 1389 M 3–7 events in the state of California, from 2011 to 2022. We first compare the observed ground motions to predictions from existing ground-motion models, namely the modified Boore and Atkinson (2008) and active crustal Next Generation Attenuation (NGA)-West2 ground-motion prediction equations, before implementing a new Bayesian model optimized for a real-time setting. Residual analysis of peak ground acceleration and peak ground velocity metrics across a host of earthquake rupture scenarios from the two ground-motion models show that the active crustal NGA-West2 model is better suited for ShakeAlert in California. In addition, the event-terms calculated using our Bayesian approach rapidly converge such that errors from earthquake magnitude estimation can be corrected for when forecasting shaking intensity in real time. Equipped with these improved ground-shaking predictions, we show that refined ShakeAlert warnings could be issued to the public within as soon as 5 s following ShakeAlert’s initial warning. This approach could be used both to reduce prediction uncertainties and thus improve ShakeAlert’s alerting decision.

Offshore Ground Motion Models for Arias Intensity and Cumulative Absolute Velocity in the Japan Trench Area

ABSTRACT This study proposes offshore ground motion models (GMMs) for Arias intensity and cumulative absolute velocity by using the seafloor ground motion database in the Japan Trench area. Whether the station is buried or not will have a great influence on the Arias intensity and cumulative absolute velocity. Compared with existing GMMs, as the magnitude increases, the energy accumulation effect of offshore GMMs is greater than that of onshore GMMs with similar magnitude and distance, and the attenuation rate is slower than other models. The proposed GMMs can be used for seismic hazard and risk analysis in the Japan Trench area.

Earthquake scenarios for building portfolios using artificial neural networks: part I—ground motion modelling

Earthquake scenarios for building portfolios using artificial neural networks: part I—ground motion modelling

Vertical ground motion model for the NGA-West2 database using deep learning method

Vertical-component of ground motions (GM) plays a significant role in seismic hazard analysis, especially for long-span structures and high-rising buildings. The former is usually predicted by empirical ground motion models (GMMs) that are developed on the basis of a preset function form and thus intensely depend on researchers' choices and prior knowledge. To overcome this issue, a deep learning-based GMM to predict the vertical component of GMs' IMs is developed in this study. 20,651 GM recordings are selected and divided into training, validation, and testing dataset based on the Next Generation Attenuation-West2 Project (NGA-West2). Comparative assessments with existing models are introduced on predicting performance indicators, IMs’ distribution with respect to seismic parameters, residuals, and variabilities. It can be concluded that the proposed model possesses better predictive power than the compared models. Meanwhile, sound physical features (e.g., magnitude scaling effects and near-fault saturation) can be observed.

Estimates of κ0 and Effects on Ground Motions in the San Francisco Bay Area

ABSTRACTGround-motion studies are a key component of seismic hazard analyses and often rely on information of the source, path, and site. Extensive research has been done on each of these parameters; however, site-specific studies are of particular interest to seismic hazard studies, especially in the field of earthquake engineering, as near-site conditions can have a significant impact on the resulting ground motion at a site. There has been much focus on the constraint of site parameters and their application to seismic hazard studies, especially in the development of ground-motion models (GMMs). Kappa is an observational parameter describing the high-frequency attenuation of spectra, and its site contribution (κ0) has shown to be a good predictor of high-frequency ground motions; however, measurements are often limited. In this study, we develop a κ0 dataset for the San Francisco Bay area (SFBA) by estimating κ0 for 228 stations, and we produce a continuous regional map of κ0. We find κ0 to range between 0.003 and 0.072 s, with larger values concentrating on the east, north, and south sides of the bay, and lower values concentrating on the west side. We also evaluate the robustness of κ0 as a site parameter and find it to correlate with peak ground acceleration. These estimates of κ0 can add predictive power to GMMs, thus increasing the accuracy of predicted ground motion and improving the robustness of ground-motion studies in the SFBA.