HPC-enabled large-scale physics-based seismic simulations as training data for AI-driven ground motion forecasting in Southern Iceland

High-performance computing (HPC) plays a central role in advancing AI-based approaches for time-critical natural hazard applications, especially in regions where observational data are limited. In seismology, the scarcity of strong-motion records for large earthquakes poses a major challenge for the development of purely data-driven ground motion models. Here, we highlight the use of HPC to generate large, high-fidelity synthetic earthquake datasets specifically tailored for training machine-learning (ML) models for rapid ground motion forecasting in Southern Iceland.

Using the CyberShake workflow on HPC systems, we compute an unprecedented ensemble of approximately 100,000 physics-based earthquake scenarios, spanning magnitudes Mw 5.0–7.4, at 350 synthetic stations across the Southern Iceland Seismic Zone and the Reykjanes Peninsula Oblique Rift. Seismic wave propagation is simulated deterministically up to 2 Hz using three alternative Earth velocity models, allowing us to systematically investigate how subsurface velocity heterogeneity influences ground motion. By exploiting seismic reciprocity, the computational cost scales with the number of virtual recording sites rather than with the number of earthquakes, making it feasible to explore tens of thousands of rupture scenarios on Tier-0 HPC systems. The resulting simulations combine multiple velocity models, dense site coverage, and designed magnitude distributions, forming a comprehensive and carefully curated training dataset.

This large HPC-generated database is then used to train machine-learning surrogate models within the Machine Learning Estimator for Ground Shaking Maps (MLESmap) framework, including both tree-based ensembles and deep neural networks. Although these ML models provide near-instantaneous predictions of ground motion intensity measures during post-event response, their reliability ultimately depends on the quality, diversity, and physical realism of the underlying training data.

Our results show that HPC-driven simulation workflows can effectively close the data gap in regions with limited observations, delivering physically grounded datasets that support robust AI models for time-critical seismic hazard assessment. More broadly, this work underscores the role of HPC not only as a computational tool for modeling extreme events, but as a cornerstone of next-generation AI-driven systems for hazard forecasting and emergency response.