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This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

Conditional ground‐motion models (CGMMs) for estimating Arias intensity (IA) for earthquakes in subduction zones are developed. The estimate of IA is conditioned in these models on the estimated peak ground acceleration (PGA), the spectral acceleration at T=1  s (SA1), time‐averaged shear‐wave velocity in the top 30 m (VS30), and magnitude (Mw). Random‐effects regressions are used to develop CGMMs for Japan, Taiwan, South America, and New Zealand. By combining the conditional models of IA with the ground‐motion models (GMMs) for PGA and SA1, the conditional models are converted to scenario‐based GMMs that can be used to estimate the median IA and its standard deviation directly for a given earthquake scenario and site conditions. The conditional scaling approach ensures the estimated IA values are consistent with a design spectrum that may correspond to above‐average spectral values for the controlling scenario. In addition, this approach captures the complex ground‐motion scaling effects found in GMMs for spectral acceleration, such as sediment‐depth effects, soil nonlinearity effects, and regionalization effects, in the developed scenario‐based models for IA. Estimates from the new scenario‐based IA models are compared to those from traditional GMMs for IA in subduction zones.

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The Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 database is used to derive a new conditional ground-motion model (CGMM) and a set of scenario-based models for estimating cumulative absolute velocity (CAV) for earthquakes in shallow crustal tectonic settings. Random-effects regressions were performed to develop the conditional model, with random effects across different earthquakes. The estimate of CAV is conditioned on the estimated peak ground acceleration (PGA), the time averaged shear-wave velocity in the top 30 m (VS30), the earthquake magnitude (Mw), and the rupture distance (Rrup). By combining the conditional CAV model with ground-motion models (GMM) in shallow crustal earthquake zones for PGA, new scenario-based models are developed for estimating the median CAV and its standard deviation, directly from an earthquake scenario and site conditions. A scenario-based CAV model captures inherently the complex ground-motion scaling effects included in the GMMs for spectral accelerations on which it is based on, such as, sediment-depth effects, soil nonlinearity effects, and regionalization effects. This approach also ensures consistency between the estimated CAV values and a design spectral acceleration response spectrum. The conditional and scenario-based models to estimate CAV are presented, and trends of the developed scenario-based models and previous traditional models for CAV are compared. Interestingly, we found a remarkable consistency between scenario-based and traditional nonconditional CAV models, when the underlain spectral GMM used in the implementation of the scenario-based model is properly constrained. Finally, we provide examples for the use of the conditional and scenario-based models in performance-based earthquake engineering.