Título: | SEISMIC IMAGE SUPER RESOLUTION | ||||||||||||
Autor: |
PEDRO FERREIRA ALVES PINTO |
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Colaborador(es): |
SERGIO COLCHER - Orientador |
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Catalogação: | 06/DEZ/2022 | Língua(s): | PORTUGUESE - BRAZIL |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=61491&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=61491&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.61491 | ||||||||||||
Resumo: | |||||||||||||
Super resolution (SR) is a topic of notable importance in domains of
assorted knowledge, such as the medical, monitoring, and security areas.
The use of deep neural networks to solve this task is something extremely
recent in the seismic field, with few references, which began to be published
less than 2 years ago. However, the literature presents a wide range of
methods, using neural networks for the super resolution of natural images.
With this in mind, the objective of this work is to explore such approaches
applied to synthetic seismic data from reservoirs. For this, models of
chronological importance in the literature were used and compared with
a classic interpolation method and with models of the literature of super
resolution of seismic images. These models are: SRCNN, RDN, the Deep
Image Prior approach and SAN. The results show that the PSNR obtained
by architectures developed for the seismic domain is equivalent to 38.23 and
the best result of the proposed architectures is 38.62, showing the progress
that such models bring to the seismic domain.
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