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Spectral decomposition is a powerful analysis technique that provides direct measurement of thin-bed tuning effects. One limitation in using spectral decomposition in volumetric analysis is the shear size of the spectral component volumes that are generated. For this reason, peak amplitude, peak frequency, and peak phase, which represent the mode of the complex spectra have proven to be three of the most useful volumetric spectral attributes. While estimates of the spectral mean, bandwidth, skewness, and kurtosis have been available in commercial formationbased spectral decomposition software for almost a decade, few if any case studies have been presented showing its value. Since many spectra are bi-vs. unimodal we find the mean spectra, bandwidth and kurtosis to have only limited interpretation value. In contrast, spectral skewness estimates quantify the asymmetry of the spectra which in turn can be correlated to multimodal behaviour due to channels, as well as the presence of upward fining, and upward coarsening sequencies.