On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signals
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Author/sCano Domingo, Carlos; Stoean, Ruxandra; Novas Castellano, Nuria; Fernández Ros, Manuel; Joya, Gonzalo; [et al.]
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.