| Summary: | Pre-print: Ground Motion Shaking Predictions Based on Machine Learning and Physics-based Simulations Earthquakes constitute a major threat to human lives and infrastructure, hence it is crucial to quickly assess the intensity of ground motions after a major seismic event. Rapid estimation of the intensity of ground vibrations is essential to assess the impact after a major earthquake occurs. The Machine Learning Estimator for Ground Shaking Maps (MLESmap) introduces an innovative approach that harnesses the predictive capabilities of Machine Learning (ML) algorithms, utilizing high-quality physics-based seismic scenarios. MLESmap aims to provide ground intensity measures within seconds following an earthquake. The inferred information can produce shaking maps of the ground providing quasi-real-time affectation information to help us explore uncertainties quickly and reliably. To develop the MLESmap technology, we used ground-motion simulations generated by the CyberShake platform. Originally designed for Southern California, this physics-based Probabilistic Seismic Hazard Methodology was migrated to the South Iceland Seismic Zone recently. Our methodology follows a three-step process: simulation, training, and deployment. By employing this approach, we can generate the next generation of ground shake maps, incorporating essential physical information derived from wave propagation, such as directivity, topography, and site effects. Remarkably, the evaluation times for MLESmap are comparable to empirical Ground Motion Models, whereas the predictive capacity of the former is superior for the Mw > 5 earthquakes. In this work, we present the application of the MLESmap methodology in South West Iceland.
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