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dc.contributor.authorCano Domingo, Carlos 
dc.contributor.authorStoean, Ruxandra
dc.contributor.authorNovas Castellano, Nuria 
dc.contributor.authorFernández Ros, Manuel 
dc.contributor.authorJoya, Gonzalo
dc.contributor.authorGázquez Parra, José Antonio 
dc.date.accessioned2022-06-29T15:55:08Z
dc.date.available2022-06-29T15:55:08Z
dc.date.issued2022-06-21
dc.identifier.issn2673-4591
dc.identifier.urihttp://hdl.handle.net/10835/13865
dc.description.abstractThe 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.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSchumann resonancees_ES
dc.subjectearthquake detectiones_ES
dc.subjectdeep learninges_ES
dc.subjectautoencoderes_ES
dc.subjectLSTMes_ES
dc.subjectRNNes_ES
dc.subjectforecastinges_ES
dc.subjectdimension reductiones_ES
dc.titleOn the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signalses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2673-4591/18/1/15es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/engproc2022018015


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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