Deep learning event detector from long-term signal variation for seismic activity warning out of Schumann resonance
Rattachement africain : es, ro. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Deep Learning (DL) has shown capability in many areas of impact on everyday life. The paper proposes a DL architecture tailored for event detection from examining the time evolution of a signal. With temporal characteristics extracted by a Convolutional Neural Network (CNN) encoder and fed as input to a recurrent neural network, the model targets the detection of a possibly occurring investigated event in the given time interval. The utility of DL methodologies to solve physical problems is demonstrated for an application of the complex experimentally-studied existing interaction between Schumann Resonance (SR) and seismic activity. SR signals are electromagnetic waves propagating along the Earth-ionosphere cavity. Intense lightning activity is continuously present at the same locations around the world, being sensitive to physical perturbation. Seismic activity modifies this steady lightning pattern. The new DL model is applied to answer the research question of whether the variation of the SR signal is truly a verifiable forerunner of seismic activity. Several parameter configurations are explored, either model-related or linked to criteria for selecting seismic events. Results show preliminary evidence about the relation between distance-intensity space and SR perturbation, and provide valuable corroboration about the sensitivity of the sensor to a specific azimuth between the observatory and the Earthquake (EQ) epicenter, hence argumentatively supporting the SR temporal characteristics as an early seismic warning. This is the first generalization of seismic disturbance as a derivative of the SR, based only on its signal time series variation, as a hypothesized precursor of the EQ event.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep learning event detector from long-term signal variation for seismic activity warning out of Schumann resonance
- Date Crossref
- 01/10/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Barcelona Supercomputing Center pays non établi dans la noticeStructure de recherche
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Universitat Politècnica de Catalunya pays non établi dans la noticeUniversité ou école supérieure
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West University of Timişoara Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Romanian Institute of Science and Technology Artificial Intelligence and Machine Learning pays non établi dans la noticeOrganisation à but non lucratif
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University of Craiova Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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University of Almería pays non établi dans la noticeUniversité ou école supérieure
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Universidad de Málaga pays non établi dans la noticeUniversité ou école supérieure
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Department of Computer Science pays non établi dans la noticeInstitution
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Ceia3 pays non établi dans la noticeInstitution
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Departamento Tecnologia Electronica pays non établi dans la noticeInstitution
Barcelona Supercomputing Center, Universitat Politècnica de Catalunya et Department of Computer Science — West University of Timişoara, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.