A ground motion prediction model for the Italian region based on a mixture of experts framework

Earthquake ground-motion prediction is critical for seismic design, risk assessment, and infrastructure resilience. Traditional empirical models often struggle to capture the complex, non-linear physical mechanisms of wave propagation and site amplification, leading to high aleatory variability and limited predictive accuracy. To address these limitations, this study proposes a ground-motion prediction model for the Italian region based on a Mixture-of-Experts (MoE) framework, utilizing XGBoost regressors as specialized experts. To ensure a robust assessment of predictive generalization, the model is developed and verified using a strict event-wise chronological splitting strategy on the ITACAext 2.0 dataset, which effectively eliminates event-level information leakage. Compared with empirical ground-motion prediction equations (GMPEs) and baseline machine-learning models, the MoE-XGB framework achieves superior predictive accuracy, significantly reducing both inter-event and intra-event variability across a wide spectral period range. Beyond statistical performance, we employ SHAP (SHapley Additive exPlanations) analysis to open the “black box” of the model, demonstrating that the MoE-XGB framework successfully learns and synthesizes physically sound seismological relationships, including magnitude scaling, geometric attenuation, and site amplification. As a high-fidelity foundational predictive engine, the MoE-XGB model provides robust, physically consistent, and fully decomposed statistical inputs that serve as a reliable basis for future regional seismic hazard assessments and performance-based engineering. The proposed framework exhibits strong transferability and scalability, offering a valuable reference for data-driven ground-motion prediction in other seismically active regions.