A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

Abstract After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically...

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Главные авторы:	Marisol Monterrubio-Velasco, Scott Callaghan, David Modesto, Jose Carlos Carrasco, Rosa M. Badia, Pablo Pallares, Fernando Vázquez-Novoa, Enrique S. Quintana-Ortí, Marta Pienkowska, Josep de la Puente
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2024-05-01
Серии:	Communications Earth & Environment
Online-ссылка:	https://doi.org/10.1038/s43247-024-01436-1
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