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On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signals
dc.contributor.author | Cano Domingo, Carlos | |
dc.contributor.author | Stoean, Ruxandra | |
dc.contributor.author | Novas Castellano, Nuria | |
dc.contributor.author | Fernández Ros, Manuel | |
dc.contributor.author | Joya, Gonzalo | |
dc.contributor.author | Gázquez Parra, José Antonio | |
dc.date.accessioned | 2022-06-29T15:55:08Z | |
dc.date.available | 2022-06-29T15:55:08Z | |
dc.date.issued | 2022-06-21 | |
dc.identifier.issn | 2673-4591 | |
dc.identifier.uri | http://hdl.handle.net/10835/13865 | |
dc.description.abstract | 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. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Schumann resonance | es_ES |
dc.subject | earthquake detection | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | autoencoder | es_ES |
dc.subject | LSTM | es_ES |
dc.subject | RNN | es_ES |
dc.subject | forecasting | es_ES |
dc.subject | dimension reduction | es_ES |
dc.title | On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signals | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2673-4591/18/1/15 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.3390/engproc2022018015 |