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\begin{document}

\title{DeepQuake --- An application of CNN for seismo-acoustic event
classification in The Netherlands}



\author[1,1,1]{Luca Trani}%
\author[1,1,1]{Giuliano Andrea Pagani}%
\author[1,1,1]{João Paulo Pereira Zanetti}%
\author[2,2,2]{Camille Chapeland}%
\author[1,1,1]{Läslo Gerardus Evers}%
\affil[1]{KNMI}%
\affil[2]{TU Delft}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\begin{abstract}
Recent developments of infrastructures and methods are major driving
forces in the advances of solid Earth sciences. The deployment of large
and dense sensor networks enables data centres to acquire data of
increased volume and quality. The analysis of such data provides
scientists with a better understanding about natural phenomena in the
subsurface. Nevertheless new challenges arise to exploit the growing
information potential. Innovative methods based on Artificial
Intelligence offer concrete opportunities to tackle those challenges. In
this paper we present an investigation of Convolutional Neural Networks
(CNN) for seismo-acoustic event classification in the Netherlands. We
designed, trained and evaluated two CNN models. Our results suggest that
as CNN inputs spectrograms are more suitable than continuous waveforms.
We discuss our findings' potential and requirements for their
operational adoption. We focus on explainability aspects and offer an
approach to pave the way for a broader uptake of Artificial Intelligence
based methods.%
\end{abstract}%



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