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GCSpectral Decomposition’s Analytical Value*
Satinder Chopra1 and Kurt J. Marfurt2
Search and Discovery Article #41260 (2013)
Posted December 23, 2013
*Adapted from the Geophysical Corner column, prepared by the authors, in AAPG Explorer, December, 2013.
Editor of Geophysical Corner is Satinder Chopra ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic
1Arcis Corp., Calgary, Canada ([email protected])
2University of Oklahoma, Norman, Oklahoma
Stratigraphers use seismic
data
in two major ways:
Using modern and paleo analogs as well as well control, the interpreter uses such boundaries and features to map seismic
facies, which in turn can be related to lithology. The
interpretation
of discrete stratigraphic features is limited by both the bandwidth and the signal-to-noise ratio of the
seismic
data
.
Unfortunately, well-resolved reflections from the top and base of subtle stratigraphic geologic boundaries occur only for thick features imaged by broadband data
. Seismically thin stratigraphic features approaching a quarter wavelength thickness give rise to composite, or “tuned,”
seismic
reflections. Direct estimation of stratigraphic thickness is more difficult, with the definition of many of the features of interest, such as channel systems, becoming more muted.
Fortunately, the tuning phenomena also can help delineate such unresolved features – specifically, the composite amplitude of a thin layer is strongest (and usually has the highest signal-to-noise ratio) at the quarter wavelength tuning frequency. Thus, if we “probe” the subsurface with the correct frequency, we can better delineate our target.
We have shown how channel features are seen clearly on a 40 Hz spectral display. The coherence attribute run on spectral data
yields much better definition of the channel features. We will illustrate the use of spectral decomposition for obtaining clearer definition of the subtle fault features in a future article.
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A previous Geophysical Corner article (Search and Discovery Article # 40454) showed how low frequency components (specifically that part of the
As the name suggests, spectral decomposition decomposes the
Spectral magnitude highlights features that are tuned, and spectral phase components enhance subtle fault and channel edges that can be used as input to subsequent There are other methods that also could be used for the purpose, such as: Each of these methods has its own applicability and limitations, and the choice of a particular method also could depend on the end objective. For example: ![]() ![]() The S-transform method is better than the continuous wavelet transform method, as it yields good temporal and spectral resolution. The matching pursuit method does not need any windowing and so yields both good temporal and spectral resolution. It is, however, computationally more expensive.
There are a number of commercial or proprietary implementations of spectral decomposition that are routinely used in the industry and are based on some variation of the above methods. Using any of the above spectral decomposition methods, the input Here, we illustrate the S-transform application of the spectral decomposition method to a case study from western Canada. In
Figure 1 we show a comparison of stratal slices through the
Note there is greater lateral variation in Spectral decomposition is an effective way of analyzing the |