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\begin{document}

\title{Applying Machine Learning to Crowd-sourced Data from Earthquake
Detective}



\author[1]{Omkar Ranadive}%
\author[1]{Vivian Tang}%
\author[1]{Kevin Chao}%
\author[1]{Suzan van der Lee}%
\affil[1]{Northwestern University}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
Dynamically triggered earthquakes and tremor generate weak seismic
signals whose detection, identification, and authentication call for a
laborious analysis. Citizen science project Earthquake Detective
leverages the eyes and ears of volunteers to detect and classify weak
signals in seismograms from potentially dynamically triggered (PDT)
events. Here, we present the Earthquake Detective data set - A
crowd-sourced set of labels on PDT earthquakes and tremor. We apply
Machine Learning to classify these PDT seismic events and explore the
challenges faced in segregating such weak signals. The algorithm
confirms that machine learning can detect signals from small
earthquakes, and newly demonstrates that this specific algorithm can
also detect signals from PDT tremor. The data set and code are available
online.%
\end{abstract}%



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