On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signals
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Cano Domingo, Carlos; Stoean, Ruxandra; Novas Castellano, Nuria; Fernández Ros, Manuel; Joya, Gonzalo; [et al.]Fecha
2022-06-21Resumen
The relationship between Schumann resonances and earthquakes was proposed more than 50 years ago; however, the experimental support has not been fully established. A considerable amount of recent studies have focused on the relationship between a single earthquake and the Schumann resonance signal variation around this earthquake, obtaining preliminary support for the existence of the link. Nonetheless, they all lack a systematic and general approach. In this research, we propose a novel methodology to detect the presence of relevant earthquakes based on the Schumann resonance. The methodology is based on a deep learning framework composed of a pretrained variational auto-encoder followed by an LSTM network and a fully connected layer with a sigmoid output. The results reveal the uncovered relationship between earthquake activity and Schumann resonance signal using the novel methodology, being the first automatic earthquake detector based on Schumann resonance signal.
Palabra/s clave
Schumann resonance
earthquake detection
deep learning
autoencoder
LSTM
RNN
forecasting
dimension reduction