Sanjay Singh Bora

This article investigates the very large ground‐motion amplitudes of peak ground acceleration (PGA) and short vibration periods on soft soil sites in high‐seismicity regions from the 2022 New Zealand National Seismic Hazard Model (NZ NSHM). For example, a PGA of 1.36 g is predicted for the 2% in 50 year exceedance probability at a site with V _{S 30} = 225 m/s in Wellington, New Zealand. Such values are in the realm of the largest globally‐recorded ground motions on soft‐soil sites. In this context, the article examines the amplitudes of these NSHM‐based ground‐motion amplitudes against historical observations, and scrutinizes the treatment of nonlinear site amplification adopted in common ground‐motion models (GMMs). Through such an examination, we form the opinion that, while the design ground motions can physically occur, there are multiple aspects that likely lead to overestimations, particularly for PGA. The most notable is the use of the equivalent‐linear method to constrain nonlinear site response for high‐intensity ground motions and associated shear strains, well beyond those for which this method is suitable. We propose adjusted nonlinear functions for PGA, for soft‐soil sites with V _{S 30} < 300 m/s, based on nonlinear simulations for New Zealand sites, which more rigorously consider effects of nonlinear soil behavior. The adopted nonlinear functions were subsequently implemented into GMMs used in the NZ NSHM, and it was illustrated that they result in a decrease in PGA hazard, which becomes more pronounced with increasing ground‐motion intensity. For example, a reduction in PGA of approximately 25% is predicted for the highest‐hazard regions of New Zealand, such as Wellington, at the 2% in 50 year exceedance probability. This study highlights the important impacts of soil nonlinearity on PGA for soft‐soil sites in high seismic hazard regions, and the need to more rigorously consider this modeling aspect in future GMM development.

Empirical correlations between ground‐motion intensity measures (IMs) are essential for engineering applications, including conditional ground‐motion selection and vector‐valued probabilistic seismic hazard analysis (VPSHA). Although previous research has thoroughly investigated interperiod correlations of response spectral ordinates and relationships between nonspectral IMs and pseudo‐spectral acceleration (PSA), the connections between engineering IMs and Fourier spectral ordinates remain inadequately characterized from an engineering seismology standpoint. This investigation establishes comprehensive empirical correlations between PSAs, Arias intensity (AI), and cumulative absolute velocity (CAV) with Fourier spectral amplitudes utilizing the NGA‐West2 database. The linear correlations are presented using total residuals of the IMs with respect to established ground‐motion models from Abrahamson et al. (2014), Bora et al. (2019), Afshari and Stewart (2016), and Campbell and Bozorgnia (2019). A partial correlation methodology isolates frequency‐dependent relationships. The results confirm earlier observations by Bora et al. (2016) and identify specific frequency ranges controlling individual IMs. Peak ground acceleration (PGA) and peak ground velocity (PGV) are predominantly influenced by higher‐frequency content, with distinct frequency ranges characterizing each parameter. Conversely, AI and CAV exhibit sensitivity across a broader, overlapping frequency band (1–3 Hz). Scenario‐dependent analysis through magnitude binning reveals systematic correlation variations, which essentially explain the regional differences in IM‐Fourier amplitude relationships. These findings illuminate the physical foundation of engineering IMs and hold substantial implications for host‐to‐target adjustments of GMMs, next‐generation GMM development, physics‐based ground‐motion simulation, and understanding of IM correlations in existing literature. The work successfully connects seismological and engineering ground‐motion characterizations.

Reliable ground-motion models are fundamental to seismic hazard assessment, yet their development is often limited by the scarcity of strong-motion recordings, particularly for large-magnitude earthquakes. In such cases, stochastic ground-motion simulations provide an efficient and versatile means to augment empirical data sets and to investigate the influence of source, path, and site effects. This study presents a comprehensive comparative evaluation of three widely used stochastic simulation tools: the Stochastic-Method SIMulation (SMSIM), which employs a point-source representation, and two finite-fault models, EXtended SIMulation (EXSIM) and Ground-Motion Simulation System. Simulations were performed under two input configurations: (1) default conditions, using regionally calibrated parameters specific to each model, and (2) identical conditions, where uniform input parameters were applied across all three models. The analysis reveals that apparent discrepancies among model predictions arise primarily from differences in parameterization rather than inherent methodological distinctions. Specifically, SMSIM requires higher stress-drop and kappa values to produce results comparable to EXSIM. When site amplification and crustal filters are treated consistently, the simulated ground motions from all three models converge closely. These findings underscore the critical importance of consistent and well-calibrated parameterization of source, path, and site effects, demonstrating that robust hazard estimation depends more on parameter harmonization than on the choice of stochastic simulation code itself.

This paper discusses the development of an adjustment factor for PGA from the 2022 update of the New Zealand (NZ) National Seismic Hazard Model (NSHM2022) and its implementation in the NZ technical specifications TS1170.5:2024 (TS1170). The study focuses on soft soil sites with VS30 ≤ 300 m/s (i.e., Site Classes IV, V, and VI in TS1170). The adjustment factor is based on nonlinear site-response simulations of NZ characteristic soft soil sites and an examination of observations from extensive national and global ground-motion databases. These simulations treat soil nonlinearity more rigorously than the approximations used in the empirical ground-motion models employed in NSHM2022. The scientific background and details of the analyses used to develop the PGA adjustment factors are documented in de la Torre et al. [1], while the focus of this paper is the parametrisation of the proposed adjustment factor for implementation into TS1170. The adjusted PGAs are compared to the PGAs obtained directly from NSHM2022, and the PGAs from the 2004 NZ seismic loading standard NZS1170.5:2004 for many cities. Application of the adjustment factor results in a reduction to PGA for all three site classes. The amount of reduction increases with increasing intensity of the NSHM2022 predicted ground motion (i.e., PGA), resulting in approximately 15-25 % reduction to the 2500-year return period PGA in the highest hazard regions of NZ. However, even with the proposed reduction factors, compared with NZS1170.5:2004, the adjusted PGAs in these high-hazard regions are still 40-50 % higher for the 500-year return-period ground motion.

This article summarizes the ground-motion characterization (GMC) model component of the 2022 New Zealand National Seismic Hazard Model (2022 NZ NSHM). The model development process included establishing a NZ-specific context through the creation of a new ground-motion database, and consideration of alternative ground-motion models (GMMs) that have been historically used in NZ or have been recently developed for global application with or without NZ-specific regionalizations. Explicit attention was given to models employing state-of-the-art approaches in terms of their ability to provide robust predictions when extrapolated beyond the predictor variable scenarios that are well constrained by empirical data alone. We adopted a “hybrid” logic tree that combined both a “weights-on-models” approach along with backbone models (i.e., metamodels), the former being the conventional approach to GMC logic tree modeling for NSHM applications using published models, and the latter being increasingly used in research literature and site-specific studies. In this vein, two NZ-specific GMMs were developed employing the backbone model construct. All of the adopted subduction GMMs in the logic tree were further modified from their published versions to include the effects of increased attenuation in the back-arc region; and, all but one model was modified to account for the reduction in ground-motion standard deviations as a result of nonlinear surficial site response. As well as being based on theoretical arguments, these adjustments were implemented as a result of hazard sensitivity analyses using models without these effects, which we consider gave unrealistically high hazard estimates.

The 2022 revision of Aotearoa New Zealand National Seismic Hazard Model (NZ NSHM 2022) has involved significant revision of all datasets and model components. In this article, we present a subset of many results from the model as well as an overview of the governance, scientific, and review processes followed by the NZ NSHM team. The calculated hazard from the NZ NSHM 2022 has increased for most of New Zealand when compared with the previous models. The NZ NSHM 2022 models and results are available online.