Abstract
In this paper we present the preliminary results of a study using a deep-learning tool named LSTM (Long Short-Term Memory) network, to classify seismic events as near-source and far-source, with the final purpose of developing efficient earthquake early warning systems. We use a similar approach as in [15], applied to a database, named Instance, containing information about 54,008 earthquakes that occurred in Italy. Although these are preliminary results, the method shows a good ability to detect far-source events with an accuracy of about \(67\%\). For near-source events, the method shows an improvable result with an accuracy of \(57\%\).
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Adebowale, A., Di Michele, F., Rubino, B. (2023). A Preliminary Result of Implementing a Deep Learning-Based Earthquake Early Warning System in Italy. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_49
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