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Estatística
Título: CONVOLUTIONAL NETWORKS APPLIED TO SEISMIC NOISE CLASSIFICATION
Autor: EDUARDO BETINE BUCKER
Colaborador(es): SERGIO COLCHER - Orientador
Catalogação: 24/MAR/2021 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=51974&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=51974&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.51974
Resumo:
Deep Learning based models, such as Convolutional Neural Networks (CNNs), have led to significant advances in several areas of computing applications. Nevertheless, this technology is still rarely applied to seismic quality prediction, which is a relevant task in hydrocarbon exploration. Being able to promptly classify noise in common shot gather(CSG) acquisitions of seismic data allows the acceptance or rejection of those aquisitions, not only saving resources but also increasing the interpretability of data. In this work, we introduce a real-world classification dataset based on 6.918 common shot gather, manually labeled by perception of specialists and researches. We use it to train a CNN classification model for seismic shot-gathers quality prediction. In our empirical evaluation, we observed an F1 Score of 95,58 percent in 10 fold cross-validation and 93,56 percent in a Holdout Test.
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