Detalhes bibliográficos
| Ano de defesa: |
2015 |
| Autor(a) principal: |
Motta, Suellen de Araujo Caduda da Silva
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| Orientador(a): |
SILVA, Aristófanes Corrêa
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| Banca de defesa: |
Fonseca Neto, João Viana da
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| Tipo de documento: |
Dissertação
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| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
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| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
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| Departamento: |
Engenharia
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| País: |
BR
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| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/286
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Resumo: |
This work presents an automatic method for fault detection in data obtained through seismic reflection method. Identifying geological faults in seismic data is critical for better understating a geological system and planning hydrocarbon exploration. Knowing that faults are discontinuities present in seismic horizons, we propose the use of geostatistical functions which are capable of indicating the amplitude variation along the volume samples, in both predetermined distances and directions. Thus, the method is based on semivariogram, semimadogram, covariogram and correlogram functions, used as representative characteristics for the samples, which will be classified as fault or "non fault" regions by the Pattern Recognition technique named Support Vector Machine (SVM). The proposed method was validated by tests made in F3 Block, a seismic data provided by OpendTect system, with up to 92.15% sensitivity and 84.33% specificity. This work also provides an extraction of fault lines method based on region growing segmentation and morphological operators applied on the classification binary resulted volume. Also tested in F3 Block, the method was able to satisfactorily extract the faults in most of the data slices. |
