In general, faults are rarely completely captured by individual seismic attributes across a 3D seismic dataset, and optimal results are in many cases obtained by combining a number of attributes. For large fault identification, in particular, appropriate seismic attributes should allow for multi-scale feature detection. Extracting a large fault into one single 3D surface object, however, is still a challenge interpreters are faced with. We propose a methodology in which the edge cube, obtained after seismic attribute generation and enhancement, is processed and analyzed using a point cloud approach. This approach increases the feasibility and potential in generating large fault surfaces even if they manifest as 3D irregular geometries.


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