We introduce “Highlight Volumes” as a tool for the interpretation of spectral decomposition data. These volumes condense the critical information from the multiple mono-frequency volumes into just two volumes: the Peak Frequency Volume and the Peak Amplitude Above Average Volume.
Spectral decomposition (Partyka et al., 1999) is a method of analyzing seismic data for stratigraphic information. It is based on Fourier’s concept that a repeating function may be constructed by summing an infinite number of monofrequency wavelets, each with their own amplitude and phase values. For the seismic interpreter, spectral decomposition can be interpretered as filtering the data with a series of nearly mono-frequency wavelets which cover the usable spectrum of the seismic data. Most often, the results are presented as time slices, horizon slices, or stratal slices (Zeng, 2007) through 3-D seismic surveys. However, analysis of spectral decomposition data may be time consuming due to the need to investigate many dozens of seismic volumes, each representing the response to a different mono-frequency filter. We introduce “Highlight Volumes” which condense the critical information from these multiple mono-frequency volumes into two volumes: the Peak Frequency Volume (Peak Freq) and the Peak Amplitude Above Average Volume (Peak Amp Above Ave).
On seismic data, the thickness of a formation is described in terms of seismic travel time through that formation. As the formation thickness decreases, the seismic data must contain greater and greater frequency content to illuminate the formation. Thus, thinner formations have more energy at higher frequencies. Conventional wisdom, therefore, holds that thinly bedded formations, such as alternating sands and shales, are tuned at higher frequencies, and thicker formations, such as shale filled channels and basinal shales surrounding deltas, are tuned at lower frequencies. Spectral decomposition produces an amplitude versus frequency spectrum at each time sample in the 3-D volume. We analyze these spectra to find the frequency at which the amplitude content is the greatest (Figure 1), and call this frequency the peak frequency. We then analyze the amplitude at the peak frequency and subtract from it the average amplitude of the entire spectrum (Figure 2) so that we may determine whether the amplitude at the peak frequency represents a small deviation from an otherwise flat spectrum, or whether it represents a true anomaly.
Our example comes from the Gulf of Mexico Basin. Figure 3 shows a horizon slice from a conventional seismic volume in which low amplitudes are shown in magenta and blue and high amplitudes in red and yellow. Much of the horizon slice shows high amplitudes (Areas A, B and C), but here is a possibly anomalous area at the near the center along the left side of the slice. The overall area of high amplitude is somewhat smaller than in Figure 3. Areas A and B remain in the high amplitude area, but the amplitudes around Point C are somewhat less. A circular band of low amplitude, generally surrounding Area A, is now more apparent.
