In-depth comparison of deep artificial neural network architectures on seismic events classification (2020)
- Authors:
- Autor USP: MELLO, RODRIGO FERNANDES DE - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.jvolgeores.2020.106881
- Subjects: ANÁLISE DE SÉRIES TEMPORAIS; REDES NEURAIS; VULCÕES; ANÁLISE DE ONDALETAS
- Keywords: Volcano Monitoring; Wavelet Transform; Deep Neural Networks
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Volcanology and Geothermal Research
- ISSN: 0377-0273
- Volume/Número/Paginação/Ano: v. 401, p. 1-16, Sep. 2020
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
CANÁRIO, João Paulo et al. In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, v. 401, p. Se 2020, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.jvolgeores.2020.106881. Acesso em: 02 mar. 2026. -
APA
Canário, J. P., Mello, R. F. de, Curilem, M., Huenupan, F., & Rios, R. A. (2020). In-depth comparison of deep artificial neural network architectures on seismic events classification. Journal of Volcanology and Geothermal Research, 401, Se 2020. doi:10.1016/j.jvolgeores.2020.106881 -
NLM
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2026 mar. 02 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881 -
Vancouver
Canário JP, Mello RF de, Curilem M, Huenupan F, Rios RA. In-depth comparison of deep artificial neural network architectures on seismic events classification [Internet]. Journal of Volcanology and Geothermal Research. 2020 ; 401 Se 2020.[citado 2026 mar. 02 ] Available from: https://doi.org/10.1016/j.jvolgeores.2020.106881 - A novel approach to quantify novelty levels applied on ubiquitous music distribution
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Informações sobre o DOI: 10.1016/j.jvolgeores.2020.106881 (Fonte: oaDOI API)
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