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\begin{document}

\title{Volcanic Earthquake Classification using Transformer Encoder and Its
Interpretability Evaluation}



\author[1]{Yugo Suzuki}%
\author[2]{Yohei Yukutake}%
\author[2]{Takao Ohminato}%
\author[1]{Masami Yamasaki}%
\author[1]{Ahyi Kim}%
\affil[1]{Yokohama City University}%
\affil[2]{Earthquake Research Institute}%


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  \date{May 24, 2024}


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\begin{abstract}
Precisely classifying earthquake types is crucial for elucidating the
relationship between volcanic earthquakes and volcanic activity.
However, traditional methods rely on subjective human judgment, which
requires considerable time and effort. To improve this, we developed a
deep learning model using a transformer encoder for a more objective and
efficient classification. Tested on Mount Asama's diverse seismic
activity, our model achieved high F1 scores (0.876 for tectonic, 0.964
for low-frequency earthquakes, and 0.995 for noise), equivalent to or
better than other methods. According to the attention weight
visualization, our model focuses on critical seismic signal features for
classification, similar to expert analysis. However, it has been
demonstrated that removing subjective elements and employing
standardized labeling of the training data based on waveform features
are necessary to enhance the interpretability of the model.
Additionally, the analyses suggest that stations near the volcanic
crater are essential for a highly interpretative and accurate
classification.%
\end{abstract}%



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