Empirical Ground-motion Models Research Articles

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.

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.

An Empirical Site Amplification Model for the North–South Seismic Belt of China

Abstract Considering 869 ground-motion recordings from moderate-to-strong earthquakes that occurred in 2007–2019, this work investigates the variation of site amplification factors and develops an empirical site amplification model of the north–south seismic belt of China. The work first regresses a simple empirical ground-motion model (GMM) for the averaged reference site (the average shear-wave velocity for the top 30 m of the Earth (VS30) is about 760 m/s) by 322 recordings of the dataset. The regressed reference site model indicates that the slow distance attenuation still exists in earthquakes of the north–south seismic belt. Then, using the derived reference site model and 869 recordings, empirical site factors are computed and an empirical site amplification model is regressed. For the earthquakes in the north–south seismic belt, the proposed site amplification model predicts the site amplification for the 50th percentile rotated pseudospectral acceleration (RotD50 SA) with a damping ratio of 5% and periods 0.01–10 s. Moreover, the difference in site amplification factors between the north–south seismic belt of China and those of the Next Generation Attenuation (NGA) database is investigated. In general, variations of site amplification factors of the north–south seismic belt of China with VS30 are less significant than those from the NGA database. Therefore, the regional differences of ground motions caused by site effects are significant for the north–south seismic belt of China. For short periods (<1.0 s) and large values of VS30 (>300 m/s), the median site amplification of the north–south seismic belt is fitted well with most model predictions and the NGA data. The residual regional differences in the north–south seismic belt of China after site corrections can be applied to develop the more efficient global GMMs.

Variability of physics-based simulated ground motions in Thessaloniki urban area and its implications for seismic risk assessment

An accurate characterization of earthquake ground motion and its variability is crucial for seismic hazard and risk analysis of spatially distributed portfolios in urban areas. In this work, a 3D physics-based numerical approach, based on the high-performance spectral element code SPEED (http://speed.mox.polimi.it/), is adopted to generate ground shaking scenarios for strong earthquakes (moment magnitude MW=6.5–7) in the Thessaloniki area (Northern Greece). These simulations account for kinematic finite-fault rupture scenarios and a 3D seismic velocity including the two main geological structures present in the area (Thessaloniki and Mygdonia basins). The numerical model is successfully validated by comparing simulated motions, on the one hand, with the recordings of a real small-magnitude (MW4.4) earthquake and, on the other, with empirical Ground Motion Models for the historical MW6.5 1978 earthquake. The sensitivity of results to the velocity model, anelastic attenuation, and non-linear soil effects is evaluated. The variability of the ground motion intensity measures in Thessaloniki as a function of the finite-fault rupture realizations (causative fault, magnitude, hypocenter location) is explored to gain insight into its potential impact on seismic risk assessment in urban areas.

Hybrid broadband ground-motion simulation validation of small magnitude active shallow crustal earthquakes in New Zealand

This article presents a comprehensive validation of the hybrid broadband ground-motion simulation approach (via the commonly used Graves and Pitarka method) in a New Zealand context with small magnitude point source ruptures using an extensive set of 5218 ground motions recorded at 212 sites from 479 active shallow crustal earthquakes across the country. Modifications to the simulation method inferred from a previous New Zealand validation are implemented, and the improvements are explicitly quantified. Empirical ground-motion models are also considered to provide a benchmark for simulation prediction accuracy and precision. Examination of intensity measure residuals identifies that the simulation method modifications lead to reduced model prediction bias and within-event variability and provides evidence toward the use of spatially varying coefficient models for simulation parameters, such as the high-frequency Brune stress parameter. Additional biases identified include, among others, underprediction of significant durations at soft soil sites and overprediction of short-period pseudo-spectral accelerations at stiff alluvial gravel and rock sites due to low-estimated 30 m time-averaged shear-wave velocity values.

Development of NGA-Sub ground-motion prediction equation of 5%-damped pseudo-spectral acceleration based on database of subduction earthquakes in Japan

We developed an empirical ground-motion model for subduction earthquakes in Japan. The model is based on the extensive, comprehensive subduction database for Japan by the Pacific Earthquake Engineering Research Center. The model predicts the RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped elastic pseudo-spectral acceleration ordinates in the selected periods ranging from 0.01 to 10 s. The model includes predictor variables considering tectonic setting (i.e. interplate and intraplate), the hypocentral depths, magnitude scaling, distance attenuation, shallow soil, and the basin responses. The magnitude scaling of interplate earthquakes is well constrained in Japan for different periods because the database includes the well-recorded large-magnitude events (i.e. the 2003 Tokachi-Oki and 2011 Tohoku earthquakes). The developed ground-motion prediction equation covers the subduction earthquakes that occurred in Japan for moment magnitudes ranging from 5.5 to 9.1 with closest distances to the fault of less than 300 km.

The older the better? The strange case of empirical ground motion models in the near-source of moderate-to-large magnitude earthquakes

This paper aims at providing a quantitative evaluation of the performance of a set of empirical ground motion models (GMMs), by testing them in a magnitude and distance range (Mw = 5.5 ÷ 7.0 and Joyner-Boore source-to-site distance Rjb ≤ 20 km) which dominates hazard in the highest seismicity areas of Italy for the return periods of upmost interest for seismic design. To this end, we made use of the very recent release of the NESS2.0 dataset (Sgobba et al. NESS2.0: an updated version of the worldwide dataset for calibrating and adjusting ground motion models in near-source. Istituto Nazionale di Geofisica e Vulcanologia (INGV), 2021. https://doi.org/10.13127/NESS.2.0), that collects worldwide near-source strong motion records with detailed metadata. After selection of an ample set of GMMs, based on either their application in past seismic hazard assessment (SHA) studies or for their recent introduction, a quantification of between- and within-event residuals of predictions with respect to records was performed, with the final aim of shedding light on the performance of existing GMMs in the near-source of moderate-to-large earthquakes, also in view of their potential improvement by taking advantage of results from 3D physics-based numerical simulations.

Within-site variability in earthquake site response

SUMMARY The within-site variability in site response is the randomness in site response at a given site from different earthquakes and is treated as aleatory variability in current seismic hazard/risk analyses. In this study, we investigate the single-station variability in linear site response at K-NET and KiK-net stations in Japan using a large number of earthquake recordings. We found that the standard deviation of the horizontal-to-vertical Fourier spectral ratio at individual sites, that is single-station horizontal-to-vertical spectral ratio (HVSR) sigma σHV,s, approximates the within-site variability in site response quantified using surface-to-borehole spectral ratios (for oscillator frequencies higher than the site fundamental frequency) or empirical ground-motion models. Based on this finding, we then utilize the single-station HVSR sigma as a convenient tool to study the site-response variability at 697 KiK-net and 1169 K-NET sites. Our results show that at certain frequencies, stiff, rough and shallow sites, as well as small and local events tend to have a higher σHV,s. However, when being averaged over different sites, the single-station HVSR sigma, that is σHV, increases gradually with decreasing frequency. In the frequency range of 0.25–25 Hz, σHV is centred at 0.23–0.43 in ln scales (a linear scale factor of 1.26–1.54) with one standard deviation of less than 0.1. σHV is quite stable across different tectonic regions, and we present a constant, as well as earthquake magnitude- and distance-dependent σHV models.

Constraints on near-source saturation models for avoiding over-saturation of response spectral ordinates in RVT-based stochastic ground-motion simulations

Inversions of empirical data and ground-motion models to find Fourier spectral parameters can result in parameter combinations that produce over-saturation of short-period response spectral ordinates. While some evidence for over-saturation in empirical data exists, most ground-motion modellers do not permit this scaling within their models. Host-to-target adjustments that are made to published ground-motion models for use in site-specific seismic hazard analyses frequently require the identification of an equivalent set of Fourier spectral parameters. In this context, when inverting response spectral models that do not exhibit over-saturation effects, it is desirable to impose constraints upon the Fourier parameters to match the scaling of the host-region model. The key parameters that determine whether over-saturation arises are the geometric spreading rate (γ) and the exponential rate within near-source saturation models (hβ). The article presents the derivation of simple nonlinear constraints that can be imposed to prevent over-saturation when undertaking Fourier spectral inversions.

Exploring Basin Amplification within the Reno Metropolitan Area in Northern Nevada Using a Magnitude 6.3 ShakeOut Scenario

ABSTRACTThe Reno metropolitan area (located within the Truckee Meadows in northern Nevada) is subjected to significant seismic risk, primarily resulting from the region’s proximity to the Mount Rose fault system and the urban area’s presence within a large, thin (<1 km thick) sedimentary basin. Numerous paleoseismic studies have shown the Mount Rose fault system has a history of producing large Holocene earthquakes. To help explore this hazard, we used SW4, a physics-based wave-equation modeling tool, to develop the Reno ShakeOut Scenario. The scenario uses a grid with a minimum spacing of 20 m with eight points per minimum wavelength to perform a full 3D simulation for a potential magnitude 6.3 earthquake within the Mount Rose fault system. The calculation assumes a minimum shear-wave velocity (VSmin) of 500 m/s and is accurate up to 3.125 Hz. Results indicate that there is a potential for widespread and variable ground shaking at modified Mercalli intensity (MMI) magnitudes between VII and VIII (very strong to severe ground shaking), with some areas achieving violent (IX and X) motions. Distributions of high shaking are controlled by proximity to the rupture, geotechnical shear-wave velocity, topography; and significantly, basin geometry. Comparisons between SW4 peak ground velocity (PGV) computations, and PGV estimates calculated from the Campbell and Bozorgnia empirical ground-motion model emphasize the degree to which very thin basins may result in greater hazard than is currently predicted. This information helps improve our understanding of regional risk by highlighting these significant basin effects and the local variability that is likely to occur with any large seismic event.

Ground motion models for shallow crustal and deep earthquakes in Hawaii and analyses of the 2018 M 6.9 Kalapana sequence

The damaging 4 May 2018 M 6.9 Kalapana earthquake and its aftershocks have provided the largest suite of strong motion records ever produced for an earthquake sequence in Hawaii exceeding the number of records obtained in the deep 2006 M 6.7 Kiholo Bay earthquake. These records provided the best opportunity to understand the processes of strong ground shaking in Hawaii from shallow crustal (< 20 km) earthquakes. There were four foreshocks and more than 100 aftershocks of M 4.0 and greater recorded by the seismic stations. The mainshock produced only a modest horizontal peak ground acceleration (PGA) of 0.24 g at an epicentral distance of 21.5 km. In this study, we evaluated the 2018 strong motion data as well as previously recorded shallow crustal earthquakes on the Big Island. There are still insufficient strong motion data to develop an empirical ground motion model (GMM) and so we developed a GMM using the stochastic numerical modeling approach similar to what we had done for deep Hawaiian (>20 km) earthquakes. To provide inputs into the stochastic model, we performed an inversion to estimate kappa, stress drops, Ro, and Q(f) using the shallow crustal earthquake database. The GMM is valid from M 4.0 to 8.0 and at Joyner–Boore (RJB) distances up to 400 km. Models were developed for eight VS30 (time-averaged shear-wave velocity in the top 30 m) values corresponding to the National Earthquake Hazards Reduction Program (NEHRP) site bins: A (1500 m/s), B (1080 m/s), B/C (760 m/s), C (530 m/s), C/D (365 m/s), D (260 m/s), D/E (185 m/s), and E (150 m/s). The GMM is for PGA, peak horizontal ground velocity (PGV), and 5%-damped pseudo-spectral acceleration (SA) at 26 periods from 0.01 to 10 s. In addition, we updated our GMM for deep earthquakes (>20 km) to include the same NEHRP site bins using the same approach for the crustal earthquake GMM.

Prediction of Ground-Motion Parameters for the NGA-West2 Database Using Refined Second-Order Deep Neural Networks

ABSTRACT One of the key elements within seismic hazard analysis is the establishment of appropriate ground-motion models (GMMs), which are used to predict the levels of ground-motion intensities by considering various parameters (e.g., source, path, and site). Many empirical GMMs were derived on the basis of a predefined linear or nonlinear equation that is heavily dependent on the a priori knowledge of a functional form that varies between the modelers’ choices. To overcome this issue, this study develops a deep neural network (DNN) trained by the recordings from the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation-West2 Project (NGA-West2) database. To this end, we collected 20,900 ground motion recordings from the database and randomly split them into the training, validation, and testing datasets. The refined second-order neuron is proposed to solve the problem, and the Adam optimizer is used to optimize the performance of the model. The prediction errors are evaluated by three performance indicators (i.e., R2, root mean square error, mean absolute error), and the predictive results are compared with previous GMMs developed based on the PEER NGA-West2 database. The between-event and within-event standard deviations (SDs) as well as total SDs are calculated and compared. Based on the comparisons, our model maintains consistent performance (e.g., the dependence of predicted intensity measures on seismological and site-specific parameters) with the compared GMM. Its relatively small total SDs, especially for longer periods, confirm that the proposed model is associated with better predictive power.

A Ground-Motion Model for the Gulf Coast Region of the United States

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

Monotonic Neural Network for Ground-Motion Predictions to Avoid Overfitting to Recorded Sites

Abstract Data-driven machine-learning approaches are being increasingly applied to construct empirical ground-motion models (GMMs). It is a standard practice to divide observational records into learning and test datasets to correctly evaluate the predictive performance of a developed model. However, in this study, we show that division based on records or earthquakes is inappropriate for evaluating the generalization performance on recorded sites when GMMs include site-condition proxies as input variables. Complex models exhibit small residuals at sites used in the training process, but exhibit large residuals at new sites owing to overfitting to the trained sites. As a simple solution, we propose a neural network model that has monotonic dependence on some of the input variables. The model successfully obtains the generalization performance on recorded sites, although it lacks ability to represent oversaturation with input variables suggested in extreme ground-motion ranges. Therefore, alternative methods should be investigated to develop robust data-driven models under general conditions. Dividing the sites into learning and test data would play a fundamental role in developing such robust models.

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

This study builds on previous site-specific hazard studies for North Iceland, specifically regarding delineation of seismic sources and seismicity parameters. Using a Monte Carlo approach to generate synthetic earthquake catalogues for North Iceland, and multiple ground motion models (GMMs) that have been proposed and used for probabilistic seismic hazard analysis (PSHA) in Iceland in the past, the variability of the resulting hazard estimates is presented in a map-form. The variability in the hazard estimates is quite large, which is a direct result of the inconsistency in the GMMs used in previous studies. We show how this is caused by the inability of these models to capture the characteristic amplitude attenuation of Icelandic earthquake ground motion with distance, thus casting doubt on the validity of the resulting PSHA of past studies. In contrast, we re-evaluated the variability of PSHA for North Iceland based on new empirical Bayesian GMMs that not only satisfy all the conditions required for use in PSHA, but also fully capture the characteristics of the existing Icelandic ground motion dataset and in addition contain elements that account for the saturation of near-fault peak ground motions at large magnitudes. The results quantify how the variability in the GMMs, contribute to the range of spatial distribution of PSHA amplitudes and uncertainties. The results show that the confidence in the PSHA values is significantly increased using the new models vs. the older ones. The confidence of the PSHA values is quantified through the coefficient of variation. The confidence is shown to be largest over distance ranges where data is most abundant. On the other hand, the confidence decreases considerably at near-fault and far-field distances, primarily because of lack of data for those distances. The findings highlight the importance of using appropriate GMMs for PSHA in Iceland and give a spatial sense of the relative levels of confidence of hazard estimates. They moreover highlight the need for a revision of the PSHA using not only the new GMMs, but also physics-based earthquake source models.

Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

ABSTRACTChoosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.

Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

This work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

Comparing Artificial Neural Networks with Traditional Ground-Motion Models for Small-Magnitude Earthquakes in Southern California

ABSTRACTTraditional, empirical ground-motion models (GMMs) are developed by prescribing a functional form between predictive parameters and ground-motion intensity measures. Machine-learning techniques may serve as a fully data-driven alternative to widely used regression techniques, as they do not require explicitly defining these relationships. Although, machine-learning methods offer a nonparametric alternative to regression methods, there are few studies that develop and assess performance of traditional versus machine-learning GMMs side by side. We compare the performance and behavior of these two approaches: a mixed-effects maximum-likelihood (MEML) model and a feed-forward artificial neural network (ANN). We develop and train both models on the same dataset from southern California. We subsequently test both models on a dataset from the 2019 Ridgecrest sequence, in a new region and on magnitudes outside the range of the training dataset, to examine model portability. Our models estimate horizontal peak ground acceleration, and the input parameters include moment magnitude (M) and hypocentral distance (Rhyp), and some include a site parameter, either VS30 or κ0.We find that, with our small set of input parameters, the ANN generally shows more site-specific predictions than the MEML model with more variation between sites, and, performs better than their corresponding MEML model, when applied “blind” to our testing dataset (in which the MEML random effects cannot be considered). Although, previous studies have found that κ0 may be a better predictor of site effects than VS30, we found similar performance, suggesting that including a site parameter may be more important than the physical meaning of the parameter. Finally, when applying our models to our Ridgecrest dataset, we find that both methods perform well; however, the MEML models perform better with the new dataset than the ANN models, suggesting that future applications of ANN models may need to consider how to accommodate model portability.

Toward Physics-Based Nonergodic PSHA: A Prototype Fully Deterministic Seismic Hazard Model for Southern California

ABSTRACT We present a nonergodic framework for probabilistic seismic-hazard analysis (PSHA) that is constructed entirely of deterministic, physical models. The use of deterministic ground-motion simulations in PSHA calculations is not new (e.g., CyberShake), but prior studies relied on kinematic rupture generators to extend empirical earthquake rupture forecasts. Fully dynamic models, which simulate rupture nucleation and propagation of static and dynamic stresses, are still computationally intractable for the large simulation domains and many seismic cycles required to perform PSHA. Instead, we employ the Rate-State earthquake simulator (RSQSim) to efficiently simulate hundreds of thousands of years of M≥6.5 earthquake sequences on the California fault system. RSQSim produces full slip-time histories for each rupture, which, unlike kinematic models, emerge from frictional properties, fault geometry, and stress transfer; all intrinsic variability is deterministic. We use these slip-time histories directly as input to a 3D wave-propagation code within the CyberShake platform to obtain simulated Fmax=0.5 Hz ground motions. The resulting 3 s spectral acceleration ground motions closely match empirical ground-motion model (GMM) estimates of median and variability of shaking. When computed over a range of sources and sites, the variability is similar to that of ergodic GMMs. Variability is reduced for individual pairs of sources and sites that repeatedly sample a single path, which is expected for a nonergodic model. This results in increased exceedance probabilities for certain characteristic ground motions for a source–site pair, while decreasing probabilities at the extreme tails of the ergodic GMM predictions. We present these comparisons and preliminary fully deterministic physics-based RSQSim–CyberShake hazard curves, as well as a new technique for estimating within- and between-event variability through simulation.

Ensemble ShakeMaps for Magnitude 9 Earthquakes on the Cascadia Subduction Zone

AbstractWe develop ensemble ShakeMaps for various magnitude 9 (M 9) earthquakes on the Cascadia megathrust. Ground-shaking estimates are based on 30 M 9 Cascadia earthquake scenarios, which were selected using a logic-tree approach that varied the hypocenter location, down-dip rupture limit, slip distribution, and location of strong-motion-generating subevents. In a previous work, Frankel et al. (2018) used a hybrid approach (i.e., 3D deterministic simulations for frequencies <1 Hz and stochastic synthetics for frequencies >1 Hz) and uniform site amplification factors to create broadband seismograms from this set of 30 earthquake scenarios. Here, we expand on this work by computing site-specific amplification factors for the Pacific Northwest and applying these factors to the ground-motion estimates derived from Frankel et al. (2018). In addition, we use empirical ground-motion models (GMMs) to expand the ground-shaking estimates beyond the original model extent of Frankel et al. (2018) to cover all of Washington State, Oregon, northern California, and southern British Columbia to facilitate the use of these ensemble ShakeMaps in region-wide risk assessments and scenario planning exercises. Using this updated set of 30 M 9 Cascadia earthquake scenarios, we present ensemble ShakeMaps for the median, 2nd, 16th, 84th, and 98th percentile ground-motion intensity measures. Whereas traditional scenario ShakeMaps are based on a single hypothetical earthquake rupture, our ensemble ShakeMaps take advantage of a logic-tree approach to estimating ground motions from multiple earthquake rupture scenarios. In addition, 3D earthquake simulations capture important features such as strong ground-motion amplification in the Pacific Northwest’s sedimentary basins, which are not well represented in the empirical GMMs that compose traditional scenario ShakeMaps. Overall, our results highlight the importance of strong-motion-generating subevents for coastal sites, as well as the amplification of long-period ground shaking in deep sedimentary basins, compared with previous scenario ShakeMaps for Cascadia.