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\begin{document}

\title{Graph-partitioning based convolutional neural network for earthquake
detection using a seismic array}



\author[1,1]{Keisuke Yano}%
\author[2,2]{Takahiro Shiina}%
\author[3,3]{Sumito Kurata}%
\author[4,4]{Aitaro Kato}%
\author[5,5]{Fumiyasu Komaki}%
\author[5,5]{Shin'ichi Sakai}%
\author[5,5]{Naoshi Hirata}%
\affil[1]{The institute of Statistical Mathematics}%
\affil[2]{Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology}%
\affil[3]{The University of Tokyo}%
\affil[4]{Earthquake Research Institute, The University of Tokyo}%
\affil[5]{University of Tokyo}%


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  \date{May 22, 2024}


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\begin{abstract}
We present a deep-learning approach for earthquake detection using
waveforms from a seismic array consisting of multiple seismographs.
Although automated, deep-learning earthquake detection techniques have
recently been developed at the single-station level, they have potential
difficulty in reducing false detections owing to the presence of local
noise inherent to each station. Here, we propose a deep-learning-based
approach to efficiently analyze the waveforms observed by a seismic
array, whereby we employ convolutional neural networks in conjunction
with graph partitioning to group the waveforms from seismic stations
within the array. We then apply the proposed method to waveform data
recorded by a dense, local seismic array in the regional seismograph
network around the Tokyo metropolitan area, Japan. Our method detects
more than \$97\$ percent of the local seismicity catalogue, with less
than \$4\$ percent false positive rate, based on an optimal threshold
value of the output earthquake probability of \$0.61\$. A comparison
with conventional deep-learning-based detectors demonstrates that our
method yields fewer false detections for a given true earthquake
detection rate. Furthermore, the current method exhibits the robustness
to poor-quality data and/or data that are missing at several stations
within the array. Synthetic tests demonstrate that the present method
has the potential to detect earthquakes even when half of the normally
available seismic data are missing. We apply the proposed method to
analyze 1-hour-long continuous waveforms and identify new seismic events
with extremely low signal-to-noise ratios that are not listed in
existing catalogs. (241words)%
\end{abstract}%



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