EGU24-15914, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine Learning Estimator for Ground-Shaking maps

Marisol Monterrubio-Velasco1, Rut Blanco1, Scott Callaghan2, Cedric Bhihe1, Marta Pienkowska3, Jorge Ejarque1, and Josep de la Puente1
Marisol Monterrubio-Velasco et al.
  • 1CASE, Barcelona Supercomputing Center, Barcelona, Spain (
  • 2Southern California Earthquake Center, California, USA
  • 3ETH, Zurich, Switzerland

The Machine Learning Estimator for Ground Shaking Maps (MLESmaps) harnesses the ground shaking inference capability of Machine Learning (ML) models trained on physics-informed earthquake simulations. It infers intensity measures, such as RotD50, seconds after a significant earthquake has occurred given its magnitude and location. 

Our methodology incorporates both offline and online phases in a comprehensive workflow. It begins with the generation of a synthetic training data set, progresses through the extraction of predictor characteristics, proceeds to the validation and learning stages, and yields a learned inference model. 

MLESmap results can complement empirical Ground Motion Models (GMMs), in particular in data-poor areas, to assess post-earthquake hazards rapidly and accurately, potentially improving disaster response in earthquake-prone regions. Learned models incorporate physical features such as directivity, topography, or resonance at a speed comparable to that of the empirical GMMs. 

In this work, we present an overview of the MLESmap methodology and its application to two distinct study areas: southern California and southern Iceland


How to cite: Monterrubio-Velasco, M., Blanco, R., Callaghan, S., Bhihe, C., Pienkowska, M., Ejarque, J., and de la Puente, J.: Machine Learning Estimator for Ground-Shaking maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15914,, 2024.