# Application of Key Seismic Spectral Attributes for Interpretation of Subsurface Channel Features

Spectral decomposition is a valuable analytical technique with applications in direct hydrocarbon detection, thickness determination and the measurement of attenuation, lithology and fluid type. Despite this impressive list of applications, spectral decomposition generates a large number of volumes for analysis which can overwhelm the seismic interpreter.

The geology illuminated by these volumes can be visualized by animating across spectral magnitudes on time, depth or horizon slices, a process that is both laborious and time-consuming. During animation, interpreters may identify two or three spectral component volumes that exhibit spatial patterns indicative of subsurface geological features. When three spectral magnitude components uniquely respond to certain geologic features, they are co-rendered using a red-green-blue (RGB) color combination. Efforts have been made to reduce the dimensionality of the numerous spectral components through principal component or independent component analysis. An alternative to color blending and dimensionality reduction is the use of statistical measures derived from the spectrum’s histogram.

It is a common observation that as the thickness of a formation decreases, the associated peak frequency slightly increases. This finding was expanded by applying a short-window discrete Fourier transform to the seismic data, demonstrating that the frequency corresponding to the peak spectral magnitude effectively describes formation thickness. Specifically, a low peak frequency indicates thick channels, while a high peak frequency suggests thin channels.

From such initial observations, we can generate several statistical measures that extract key components of the seismic spectrum. These attributes include peak magnitude, peak frequency and peak phase. An additional effective attribute is the measurement of peak magnitude above the mean spectrum, which is useful for delineating high amplitude tuning events. Similarly, the bandwidth is defined as the frequency distance between the leftmost and rightmost values of 1/√2 of the peak magnitude.

Figure 1 defines some of the aforementioned terms for a typical magnitude spectrum, where the peak frequency, or the mode of the spectrum, indicates the location of the peak spectral magnitude and is often associated with the tuning thickness. The mean magnitude represents the average of all computed magnitudes. The peak magnitude above the mean is calculated by subtracting the mean magnitude spectrum for each sample from the peak magnitude.

## Application

Let us now investigate the application of these attributes to actual seismic data and examine their utility for seismic interpreters. We aim to show how peak frequency and peak magnitude volumes can be effectively co-rendered, and how the inclusion of multispectral energy ratio coherence in these co-rendered displays can enhance context and add value to their interpretation. These displays are produced for both the input seismic data and its spectrally balanced counterpart.

Log in to Submit a Comment

## Comments (0)