Título: | FAULT MESHING GENERATION IN SEISMIC DATA BY COMPETITIVE LEARNING | |||||||
Autor: |
MARCOS DE CARVALHO MACHADO |
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Colaborador(es): |
MARCELO GATTASS - Orientador |
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Catalogação: | 10/JUL/2008 | 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=11889&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=11889&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.11889 | |||||||
Resumo: | ||||||||
Manual fault mapping from 3D seismic data is a
time-consuming task. A plethora of seismic attributes has
been proposed to enhance the discontinuity measures
associated with faults. However, faults viewed through these
attributes appear more like trends than well-defined,
continuous surfaces, posing obstacles to the automation of
the fault modeling process. This thesis explores the use of
Competitive Learning techniques in fault extraction and
visualization. The proposed strategy starts with a
pre-computed fault attribute and consists of three
steps. In the first, the uniformly sampled 3D fault
attribute data are converted into a graph using Growing
Neural Gas, a Competitive Learning algorithm. In the
second step, the graph is submitted to a segmentation
process in order to extract a set of subgraphs, each one
compatible with a fault surface. In the third step, the
Open Neural Meshes algorithm is used to build a triangulated
mesh for each previously identified surface. Open Neural
Meshes is a Competitive Learning algorithm proposed in this
thesis, which builds a mesh from a probability function
with no-hole open surface topology. Examples with 2D and 3D,
synthetic and real data are presented. Another Competitive
Learning application introduced in this thesis is the
generation of geologic meshes. These meshes can be used to
simulate fluid flows in subsurface reservoirs.
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