3-D visualization

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Seismic Data Analysis
Series Investigations in Geophysics
Author Öz Yilmaz
DOI http://dx.doi.org/10.1190/1.9781560801580
ISBN ISBN 978-1-56080-094-1
Store SEG Online Store

Picking horizons and faults and then abandoning the 3-D image volume no longer is practiced in interpreting 3-D seismic data. Horizon picking amounts to interpreting seismic traveltimes, and as such, it yields the knowledge for structural geology, only. The knowledge for stratigraphy, depositional environment and history, and even lithology can only be inferred by the combined interpretation of traveltimes and amplitudes contained in the seismic image volume.

3-D visualization facilitates combined interpretation of multiple volumes of data — image volumes, velocity volumes, and attribute volumes such as those derived from amplitude variation with offset (AVO) analysis and acoustic impedance estimation. Rapid detection of amplitude anomalies, and understanding and correlating the geometries of the various layer boundaries and faults are made possible by the power of 3-D visualization. Combining visualization with interpretation tools such as opacity removal, seed detection, and edge detection enable the interpreter to get the most out of the data volumes.

3-D visualization of seismic amplitudes is based on the concept of representing each sample in the data volume by a 3-D object called voxel. The voxel representation actually is an extension of a 2-D representation of a sample by a pixel. Each voxel is color coded by the amplitude of the sample associated with it.

Figure 7.5-6a shows a single depositional unit carved out from an image volume. Note the distinctive pattern of the basin-edge faults. Representation of these faults as a set of surfaces in the absence of the depositional unit would only provide a limited understanding of the structural geology of the subsurface. Figure 7.5-6b shows an example of enhancing structural features such as the intensive fault pattern over the survey area by introducing opacity to the time slab from the image volume. The fault pattern also can be seen on a map view as in Figure 7.5-6c.

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