SSAM y C3
How do the SSAM and C3 methods differ? Each method occupies, as it were, a separate chapter in the history of interpretive processing. Each of the two processes goes in a different direction, so one process never could evolve from the other. We make this clear by the following analogy: A cross section through the earth looks like an antique piece of knotty pine wood, such as is found on an old New England tabletop. The wood grain represents the rock layers. The wood had been warped, worm-eaten, broken and split, and then poorly put back together with glue and nails. The splits and breaks represent unconformities and faults in the geology.
The SSAM method can be described by extending the analogy as follows: A ripsaw is designed to saw wood along the grain. If we want to extract a particular layer that lies between two distinct grains, we use a ripsaw. We carefully cut out the layer by sawing along its upper and lower edges. It takes skill to follow the grain with the ripsaw. Once the targeted layer is cut out from the wood, we can examine that layer by eye to find the wormholes, the glued places, and all the other imperfections.
The C3 method can be described with another extended analogy. A crosscut saw is designed to saw wood across the grain. With a crosscut saw, the old tabletop simply can be cut systematically across the wood grain in thin slices. By this process, all the wormholes, splits, and breaks in the wood are revealed. No particular skill is involved except to make straight parallel cuts. The elaborate handwork required in SSAM to follow the curves of a particular grain is not required in C3, and no choice of any particular layer is required.
In conclusion, both the SSAM and C3 processes represent image enhancement. However, the SSAM process, in which the main implement is comparable to a ripsaw, is fundamentally different from the C3 process, in which the main implement is like a crosscut saw. No amount of perfection or evolution of one process ever could lead to the other process.
Let us now summarize the seismic sequence attribute map. With SSAM, we select a horizon (a geologic horizon) and our output is another horizon (an attribute horizon). For SSAM, we do the following:
1) Input: We must specify either a reference horizon or two horizons that define a layer. We must define the time interval from which attributes will be extracted. We can use
- a constant time window
- a time window centered about a single horizon
- specified amounts of time above and below a single horizon
- the interval bounded by two horizons
2) Computation: We calculate the attribute values over the interval we specify. That is, the processing runs lengthwise to the given horizon or horizons, along the grain.
3) Output: The output is the enhanced horizon, called an attribute horizon, which is stored in a horizon data file. We can display this attribute horizon, posting it on seismic sections and on the base map. In addition, we can manipulate it as we would any other horizon by smoothing it, contouring it, and so on. In map view, especially, attribute horizons can make subtle statistical patterns obvious and can guide or validate our interpretation. In horizon image map view, we can display the attribute horizon overlaid with a contour map of the corresponding time horizon or another attribute horizon.
Next, we shall summarize the coherence cube. With coherence-cube processing, we select a 3D image (i.e., a cube of, say, amplitude data), and our output is another 3D image (a cube of coherence attribute data). Coherence processing gives us a new way to view seismic data. Now we can see the degree of similarity from trace to trace. We can interpret the coherence data as well as the amplitude data, and using both types of data makes interpretation easier and more reliable. Coherence processing works with 3D volumes (cubes of data), whereas SSAM works with 2D bent surfaces (horizons). Thus, C3 and SSAM differ from each other conceptually. For C3, we do the following.
1) Input: We must select a vertical seismic file and specify the areal extents of the data to be used. In other words, we specify a 3D volume (i.e., a cube) of data (say, an image made up of the amplitudes resulting from the seismic processing).
2) Computation: Coherence processing calculates by symmetrically cutting through the data, trace by trace, without regard to horizons. That is, coherence processing runs crosswise to the horizons, across the grain. The result is a measure of the data similarity from trace to trace.
3) Output: The output is a new 3D volume of data (the enhanced image made up of coherence attributes). The advantage of coherence data is that it reveals and heightens lateral changes. Faults and stratigraphic changes often stand out as prominent anomalies in otherwise homogeneous data.
|Sección previa||Siguiente sección|
|Cubo de coherencia (C3)||Apéndice L: Diseño de la transformada de Hilbert|
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También en este capítulo
- Procesamiento interpretativo
- Atributos sísmicos
- Atributos instantáneos
- Mapa de atributos de secuencia sísmica (SSAM)
- Cubo de coherencia (C3)
- Apéndice L: Diseño de la transformada de Hilbert
- Apéndice M: Ejercicios