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\begin{document}

\title{Unsupervised learning of seismic wavefield features: clustering
continuous array seismic data during the 2009 L'Aquila earthquake}



\author[1]{Peidong Shi}%
\author[2]{Piero Poli}%
\affil[1]{Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes}%
\affil[2]{Université Grenoble-Alpes, Isterre Laboratory}%


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  \date{May 22, 2024}


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\begin{abstract}
We apply unsupervised machine learning to three years of continuous
seismic data to unravel the evolution of seismic wavefield properties in
the period of the 2009 L'Aquila earthquake. To obtain sensible
representations of wavefield properties variations, wavefield features,
i.e. entropy, coherency, eigenvalue variance, and first eigenvalue, are
extracted from the covariance matrix analysis of continuous array
seismic data. The defined wavefield features are insensitive to
site-dependent local noise, and can inform the spatiotemporal properties
of seismic waves generated by sources inside the array. We perform a
sensitivity analysis of these wavefield features and build unsupervised
learning based on the uncorrelated features to track the evolution of
source properties. By clustering the wavefield features, our
unsupervised analysis avoids explicit physical modeling (e.g. location
of events, magnitude estimation) and can naturally separate peculiar
patterns solely from continuous seismic data. The unsupervised learning
of wavefield features reveals distinct clusters well correlated with
different periods of the seismic cycle.%
\end{abstract}%



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