Hybrid Broadband Ground‐Motion Simulation Using Neural Networks with Spatial, Inter‐Period, and Cross‐Component Correlations

ABSTRACT

Simulated ground motions are increasingly used in earthquake engineering, particularly in regions with sparse strong‐motion recordings where constraining non‐ergodic ground‐motion models (GMMs) remains challenging. Physics‐based simulations (PBS) can reproduce key source and wave‐propagation effects but are often limited to low frequencies, whereas stochastic methods are computationally efficient but typically lack physically coherent three‐component behaviour and realistic near‐fault features. Here we develop a hybrid broadband framework with three main innovations. These comprise: (i) an enhanced artificial neural network (ANN) that predicts short‐period spectral accelerations (SAs) from long‐period PBS SAs and scalar source‐site predictor variables while jointly modelling the three ground‐motion components; (ii) a transfer learning strategy that enables regional calibration in data‐limited settings; and (iii) an explicit multivariate correlation model for within‐event residuals that introduces realistic broadband dependence across space, periods, and components. The approach is validated using regional 3D PBS of the M _w 6.5 and M _w 6.4 June 2000 South Iceland earthquakes. For this case study, the resulting broadband fields reproduce observed short‐period attenuation and near‐fault saturation, are consistent with local Icelandic GMM trends, and preserve physically plausible directionality and component ratios comparable to empirical models. Inter‐period correlations of within‐event residuals follow established empirical trends and, importantly, remain continuous across the stochastic–PBS transition, while spatial correlation ranges are comparable to published observations. Owing to its computational efficiency, the framework provides a practical basis for generating spatially and spectrally coherent broadband ground‐motion fields for scenario‐based hazard and risk applications.