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\begin{document}

\title{Machine Learning based Estimator for Ground Shaking Maps in South
Iceland Seismic Zone (SISZ)}



\author[1]{Marisol Monterrubio-Velasco}%
\author[1]{Rut Blanco-Prieto}%
\author[1]{Scott Callaghan}%
\author[1]{David Modesto}%
\author[1]{Josep de la Puente}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\begin{abstract}
\href{https://www.researchsquare.com/article/rs-3299183/v1}{Pre-print:
Ground Motion Shaking Predictions Based on Machine Learning and
Physics-based Simulations}

\par\null

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 \textgreater{}
5 earthquakes.

In this work, we present the application of the MLESmap methodology in
South West Iceland.%
\end{abstract}%



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