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Artigo de periodico
Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images (2023)
Rubo, Rafael Andrello; Michelon, Mateus Fontana; Carneiro, Cleyton de Carvalho
Source: Earth Science Informatics. Unidade: EP
Subjects: PETROGRAFIA, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RUBO, Rafael Andrello e MICHELON, Mateus Fontana e CARNEIRO, Cleyton de Carvalho. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images. Earth Science Informatics, p. 10 2023, 2023Tradução . . Disponível em: https://doi.org/10.1007/s12145-022-00901-9. Acesso em: 09 out. 2024.
APA
Rubo, R. A., Michelon, M. F., & Carneiro, C. de C. (2023). Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images. Earth Science Informatics, 10 2023. doi:10.1007/s12145-022-00901-9
NLM
Rubo RA, Michelon MF, Carneiro C de C. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images [Internet]. Earth Science Informatics. 2023 ;10 2023.[citado 2024 out. 09 ] Available from: https://doi.org/10.1007/s12145-022-00901-9
Vancouver
Rubo RA, Michelon MF, Carneiro C de C. Carbonate lithofacies classification in optical microscopy: a data‑centric approach using augmentation and GAN synthetic images [Internet]. Earth Science Informatics. 2023 ;10 2023.[citado 2024 out. 09 ] Available from: https://doi.org/10.1007/s12145-022-00901-9
Artigo de periodico
Mechanically oscillating sample under magnetic field gradients: MOS-NMR (2022)
Oliveira, Éverton Lucas de; Marassi, Agide Gimenez; Ferreira, Arthur Gustavo de Araújo ; Vidoto, Edson Luiz Géa; Amorim, Aparecido Donizeti Fernandes de; Trevizan, Willian Andrighetto; Bonagamba, Tito José
Source: Journal of Magnetic Resonance Open. Unidade: IFSC
Subjects: IMAGEM POR RESSONÂNCIA MAGNÉTICA, PETROGRAFIA, MATERIAIS POROSOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Éverton Lucas de et al. Mechanically oscillating sample under magnetic field gradients: MOS-NMR. Journal of Magnetic Resonance Open, v. 12-13, p. 100084-1-100084-8, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.jmro.2022.100084. Acesso em: 09 out. 2024.
APA
Oliveira, É. L. de, Marassi, A. G., Ferreira, A. G. de A., Vidoto, E. L. G., Amorim, A. D. F. de, Trevizan, W. A., & Bonagamba, T. J. (2022). Mechanically oscillating sample under magnetic field gradients: MOS-NMR. Journal of Magnetic Resonance Open, 12-13, 100084-1-100084-8. doi:10.1016/j.jmro.2022.100084
NLM
Oliveira ÉL de, Marassi AG, Ferreira AG de A, Vidoto ELG, Amorim ADF de, Trevizan WA, Bonagamba TJ. Mechanically oscillating sample under magnetic field gradients: MOS-NMR [Internet]. Journal of Magnetic Resonance Open. 2022 ; 12-13 100084-1-100084-8.[citado 2024 out. 09 ] Available from: https://doi.org/10.1016/j.jmro.2022.100084
Vancouver
Oliveira ÉL de, Marassi AG, Ferreira AG de A, Vidoto ELG, Amorim ADF de, Trevizan WA, Bonagamba TJ. Mechanically oscillating sample under magnetic field gradients: MOS-NMR [Internet]. Journal of Magnetic Resonance Open. 2022 ; 12-13 100084-1-100084-8.[citado 2024 out. 09 ] Available from: https://doi.org/10.1016/j.jmro.2022.100084
Artigo de periodico
Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images (2019)
Rubo, Rafael Andrello; Carneiro, Cleyton de Carvalho ; Michelon, Mateus Fontana; Gioria, Rafael dos Santos
Source: Journal of Petroleum Science and Engineering. Unidade: EP
Subjects: INTELIGÊNCIA ARTIFICIAL, PETROGRAFIA, ROCHAS SEDIMENTARES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RUBO, Rafael Andrello et al. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. Journal of Petroleum Science and Engineering, v. 183, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.petrol.2019.106382. Acesso em: 09 out. 2024.
APA
Rubo, R. A., Carneiro, C. de C., Michelon, M. F., & Gioria, R. dos S. (2019). Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images. Journal of Petroleum Science and Engineering, 183. doi:10.1016/j.petrol.2019.106382
NLM
Rubo RA, Carneiro C de C, Michelon MF, Gioria R dos S. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images [Internet]. Journal of Petroleum Science and Engineering. 2019 ;183[citado 2024 out. 09 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382
Vancouver
Rubo RA, Carneiro C de C, Michelon MF, Gioria R dos S. Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images [Internet]. Journal of Petroleum Science and Engineering. 2019 ;183[citado 2024 out. 09 ] Available from: https://doi.org/10.1016/j.petrol.2019.106382

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