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Abstract

The proximity of fast‐slipping crustal faults to urban areas may result in pulse‐like ground motions from rupture directivity, which can contribute to increased levels of damage even for engineered structures. Systematic modeling of directivity within probabilistic seismic hazard analysis (PSHA) remains challenging to implement at the regional scale, despite the availability of directivity models in the literature. In the process of developing the 2022 National Seismic Hazard Model for New Zealand (2022 NSHM), we explored the feasibility and impact of modeling directivity for PSHA at a national scale using the previous generation 2010 NSHM. The results of this analysis allowed us to quantify the impact of directivity on the resulting seismic hazard maps for New Zealand and gain insights into the factors that contribute to the expected increases (and decreases) in ground‐motion level. For the 2022 NSHM, the earthquake rupture forecast (ERF) seismogenic source models introduced enormous challenges for directivity modeling due to the abundance of large multisegment or multifault ruptures with complex geometries. To overcome these challenges, we applied a machine learning‐based strategy to “overfit” an artificial neural network to capture the distributions of directivity amplification and its variability for each unique rupture in the earthquake rupture forecast. This produces a compact representation of the spatial fields of amplification that are computationally efficient to generate within a complete PSHA calculation for the 2022 NSHM. This flexible and reproducible framework facilitates the implementation of directivity in PSHA at a regional scale for complex ERF source models and opens up the possibility of more complex characterization of epistemic uncertainties for near‐source ground motion in practice.