ALBERT

Description: The stochastic method of ground-motion simulation assumes that the energy in a target spectrum is spread over a duration D T . D T is generally decomposed into the duration due to source effects ( D S ) and to path effects ( D P ). For the most commonly used source, seismological theory directly relates D S to the source corner frequency, accounting for the magnitude scaling of D T . In contrast, D P is related to propagation effects that are more difficult to represent by analytic equations based on the physics of the process. We are primarily motivated to revisit D T because the function currently employed by many implementations of the stochastic method for active tectonic regions underpredicts observed durations, leading to an overprediction of ground motions for a given target spectrum. Further, there is some inconsistency in the literature regarding which empirical duration corresponds to D T . Thus, we begin by clarifying the relationship between empirical durations and D T as used in the first author’s implementation of the stochastic method, and then we develop a new D P relationship. The new D P function gives significantly longer durations than in the previous D P function, but the relative contribution of D P to D T still diminishes with increasing magnitude. Thus, this correction is more important for small events or subfaults of larger events modeled with the stochastic finite-fault method.

Description: The stochastic method of ground-motion simulation specifies the amplitude spectrum as a function of magnitude ( M ) and distance ( R ). The manner in which the amplitude spectrum varies with M and R depends on physical-based parameters that are often constrained by recorded motions for a particular region (e.g., stress parameter, geometrical spreading, quality factor, and crustal amplifications), which we refer to as the seismological model. The remaining ingredient for the stochastic method is the ground-motion duration. Although the duration obviously affects the character of the ground motion in the time domain, it also significantly affects the response of a single-degree-of-freedom oscillator. Recently published updates to the stochastic method include a new generalized double-corner-frequency source model, a new finite-fault correction, a new parameterization of duration, and a new duration model for active crustal regions. In this article, we augment these updates with a new crustal amplification model and a new duration model for stable continental regions. Random-vibration theory (RVT) provides a computationally efficient method to compute the peak oscillator response directly from the ground-motion amplitude spectrum and duration. Because the correction factor used to account for the nonstationarity of the ground motion depends on the ground-motion amplitude spectrum and duration, we also present new RVT correction factors for both active and stable regions. Online Material: Files of coefficients for evaluating distance ( D rms ), time-domain–to–random-vibration ratios, and SMSIM parameter files.

Description: Because of the limited number of strong-motion records that have measured ground response at large strains, any statistical analyses of seismic site-response models subject to strong ground motions are severely limited by a small number of observations. Recent earthquakes in Japan, including the M w  9.0 Tohoku earthquake of March 2011, have substantially increased the observations of strong-motion records that can be used to compare alternative site-response models at large strains and can subsequently provide insight into the accuracy and precision of site-response models. Using the Kiban-Kyoshin network (KiK-net) downhole array data in Japan, we analyze the accuracy (bias) and variability (precision) resulting from common site-response modeling assumptions, and we identify critical parameters that significantly contribute to the uncertainty in site-response analyses. We perform linear and equivalent-linear site-response analyses at 100 KiK-net sites using 3720 ground motions ranging in amplitude from weak to strong; 204 of these records have peak ground accelerations greater than at the ground surface. We find that the maximum shear strain in the soil profile, the observed peak ground acceleration at the ground surface, and the predominant spectral period of the surface ground motion are the best predictors of where the evaluated models become inaccurate and/or imprecise. The peak shear strains beyond which linear analyses become inaccurate in predicting surface pseudospectral accelerations (PSA; presumably as a result of nonlinear soil behavior) are a function of vibration period and are between 0.01% and 0.1% for periods 〈0.5 s. Equivalent-linear analyses become inaccurate at peak strains of ~0.4% over this range of periods. We find that, for the sites and ground motions considered, site-response residuals at spectral periods 〉0.5 s do not display noticeable effects of nonlinear soil behavior. Online Material: Site-specific information and model residuals at 100 KiK-net stations.

Description: Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic-hazard analysis usually relies on ground-motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time-averaged shear-wave velocity to 30 m ( V S 30 ) and basin depth parameters. Because V S 30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which V S 30 has been measured to address effects of V S 30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground-motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured V S 30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.

Description: This article summarizes the geotechnical effects of the 25 April 2015 M  7.8 Gorkha, Nepal, earthquake and aftershocks, as documented by a reconnaissance team that undertook a broad engineering and scientific assessment of the damage and collected perishable data for future analysis. Brief descriptions are provided of ground shaking, surface fault rupture, landsliding, soil failure, and infrastructure performance. The goal of this reconnaissance effort, led by Geotechnical Extreme Events Reconnaissance, is to learn from earthquakes and mitigate hazards in future earthquakes.

Description: For many earthquake engineering applications, site response is estimated through empirical correlations with the time-averaged shear-wave velocity to 30 m depth ( V S 30 ). These applications therefore depend on the availability of either site-specific V S 30 measurements or V S 30 maps at local, regional, and global scales. Because V S 30 measurements are sparse, a proxy frequently is needed to estimate V S 30 at unsampled locations. We present a new V S 30 map for California, which accounts for observational constraints from multiple sources and spatial scales, such as geology, topography, and site-specific V S 30 measurements. We apply the geostatistical approach of regression kriging (RK) to combine these constraints for predicting V S 30 . For the V S 30 trend, we start with geology-based V S 30 values and identify two distinct trends between topographic gradient and the residuals from the geology V S 30 model. One trend applies to deep and fine Quaternary alluvium, whereas the second trend is slightly stronger and applies to Pleistocene sedimentary units. The RK framework ensures that the resulting map of California is locally refined to reflect the rapidly expanding database of V S 30 measurements throughout California. We compare the accuracy of the new mapping method to a previously developed map of V S 30 for California. We also illustrate the sensitivity of ground motions to the new V S 30 map by comparing real and scenario ShakeMaps with V S 30 values from our new map to those for existing V S 30 maps. Online Material: California statewide V S 30 and ShakeMaps for Loma Prieta.

Description: Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic-hazard analysis usually relies on ground-motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time-averaged shear-wave velocity to 30 m ( V S 30 ) and basin depth parameters. Because V S 30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which V S 30 has been measured to address effects of V S 30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground-motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured V S 30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.

Description: We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived V S 30 , compound topographic index, and magnitude-adjusted peak ground acceleration (PGA) from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across six countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving the model’s performance. We inspected 14 geospatial proxies for soil density and soil saturation; the most promising of these are slope-derived V S 30 , modeled water table depth, distance to coast, distance to river, distance to the closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than PGA as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the receiver operating characteristic curve, and the Brier score. The best-performing model in a coastal setting uses distance to the coast but is problematic for regions away from the coast. The second best model, using PGV, V S 30 , water table depth, distance to the closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.

Description: Ground‐motion model (GMM) selection and weighting introduce a significant source of uncertainty in U.S. Geological Survey (USGS) seismic hazard models. The increase in moderate moment magnitude induced earthquakes (Mw 4–5.8) in Oklahoma and Kansas since 2009 caused by increased wastewater injection related to oil and gas production (Keranen et al., 2013, 2014; McNamara, Hayes, et al., 2015; Weingarten et al., 2015) provides useful near‐source (≤40  km) instrumental ground‐motion observations for comparisons between central and eastern United States (CEUS) induced (Rennolet et al., 2017) and tectonic (Goulet et al., 2014) earthquakes. In this study, we evaluate more than 50 GMMs using two well‐established probabilistic scoring methods: log likelihood (LLH) (Scherbaum et al., 2004, 2009) and multivariate LLH (MLLH) (Mak et al., 2017). The LLH approach compares the mean and standard deviation (σ) of the observed and modeled ground motions. The MLLH approach advances the LLH method by considering the variability (φ,τ) of multiple correlated variables, namely intra‐ (within) and inter‐ (between) event residuals.For the probabilistic scoring GMM evaluation methods (LLH, MLLH), we compute horizontal‐component peak ground acceleration (PGA) and 1‐s pseudospectral acceleration (PSA1.0) total residuals using GMM software (nshmp‐haz) recently implemented by the USGS National Seismic Hazard Model Project (NSHMP). We observe from LLH and MLLH scores that (1) newer GMMs with lower standard deviations (σ,φ,τ) score better than older GMMs with higher published uncertainty; (2) 2014 CEUS GMMs score better for CEUS tectonic earthquakes than induced earthquakes; (3) Next Generation Attenuation‐West2 Project (NGA‐West2), Grazier (2017, referred as G17), and Atkinson (2015, referred as A15) GMMs score well for CEUS induced earthquake ground motions; and (4) Next Generation Attenuation‐East Project (NGA‐East) GMMs score well for CEUS tectonic earthquake ground motions. We also use the LLH and MLLH scores to evaluate GMM weights applied in past USGS seismic hazard forecasts and to inform weighting of GMMs in future seismic hazard forecasts.

Description: We compare estimates of the empirical transfer function (ETF) to the plane SH-wave theoretical transfer function (TTF) within a laterally constant medium for invasive and noninvasive estimates of the seismic shear-wave slownesses at 13 Kiban-Kyoshin network stations throughout Japan. The difference between the ETF and either of the TTFs is substantially larger than the difference between the two TTFs computed from different estimates of the seismic properties. We show that the plane SH-wave TTF through a laterally homogeneous medium at vertical incidence inadequately models observed amplifications at most sites for both slowness estimates, obtained via downhole measurements and the spectral analysis of surface waves. Strategies to improve the predictions can be separated into two broad categories: improving the measurement of soil properties and improving the theory that maps the 1D soil profile onto spectral amplification. Using an example site where the 1D plane SH-wave formulation poorly predicts the ETF, we find a more satisfactory fit to the ETF by modeling the full wavefield and incorporating spatially correlated variability of the seismic properties. We conclude that our ability to model the observed site response transfer function is limited largely by the assumptions of the theoretical formulation rather than the uncertainty of the soil property estimates.