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\begin{document}

\title{Deep-learning-based phase picking for volcano seismicity}



\author[1]{Yiyuan Zhong}%
\author[2]{Yen Joe Tan}%
\affil[1]{The Chinese University of Hong Kong}%
\affil[2]{Earth and Environmental Sciences Programme, Faculty of Science, The Chinese University of Hong Kong}%


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  \date{May 27, 2024}


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\begin{abstract}
The application of deep-learning-based seismic phase pickers for
earthquake monitoring has surged in recent years. However, the efficacy
of these models when applied to monitoring volcano seismicity has yet to
be evaluated. Here, we first compile a dataset of seismic waveforms from
various volcanoes globally. We then show that the performances of two
widely used deep-learning pickers deteriorate systematically as the
earthquakes' frequency content decreases. Therefore, the performances
are especially poor for long-period earthquakes often associated with
fluid/magma movement. Subsequently, we train new models which perform
significantly better, including when tested on volcanic earthquake
waveforms from northern California where no training data are used and
tectonic low-frequency earthquakes along the Nankai Trough. Our
model/workflow can be applied to improve monitoring of volcano
seismicity globally while our compiled dataset can be used to benchmark
future methods for characterizing volcano seismicity, especially
long-period earthquakes which are difficult to monitor.%
\end{abstract}%



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