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Estatística
Título: SEISMIC IMAGE SUPER RESOLUTION
Autor: PEDRO FERREIRA ALVES PINTO
Colaborador(es): SERGIO COLCHER - Orientador
Catalogação: 06/DEZ/2022 Língua(s): PORTUGUESE - BRAZIL
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.
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
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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