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\begin{document}

\title{Earthquake Early Warning using 3 seconds of records on a single station}



\author[1]{Pablo Lara}%
\author[1]{Quentin Bletery}%
\author[2]{Jean-Paul Ampuero}%
\author[3]{Adolfo Inza}%
\affil[1]{Université Côte d'Azur, IRD, CNRS, Observatoire de la Côte d'Azur, Geoazur}%
\affil[2]{Institut de Recherche pour le Développement}%
\affil[3]{Instituto Geofísico del Perú}%


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  \date{May 18, 2024}


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\begin{abstract}
We introduce the Ensemble Earthquake Early Warning System (E3WS), a set
of Machine Learning algorithms designed to detect, locate and estimate
the magnitude of an earthquake using 3 seconds of P waves recorded by a
single station. The system is made of 6 Ensemble Machine Learning
algorithms trained on attributes computed from ground acceleration time
series in the temporal, spectral and cepstral domains. The training set
comprises datasets from Peru, Chile, Japan, and the STEAD global
dataset. E3WS consists of three sequential stages: detection, P-phase
picking and source characterization. The latter involves magnitude,
epicentral distance, depth and back-azimuth estimation. E3WS achieves an
overall success rate in the discrimination between earthquakes and noise
of 99.9\%, with no false positive (noise mis-classified as earthquakes)
and very few false negatives (earthquakes mis-classified as noise). All
false negatives correspond to M [?] 4.3 earthquakes, which are unlikely to
cause any damage. For P-phase picking, the Mean Absolute Error is 0.14
s, small enough for earthquake early warning purposes. For source
characterization, the E3WS estimates are virtually unbiased, have better
accuracy for magnitude estimation than existing single-station
algorithms, and slightly better accuracy for earthquake location. By
updating estimates every second, the approach gives time-dependent
magnitude estimates that follow the earthquake source time function.
E3WS gives faster estimates than present alert systems relying on
multiple stations, providing additional valuable seconds for potential
protective actions.%
\end{abstract}%



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