Abstract

Spectral decomposition of seismic data transforms seismic amplitudes as a function of space and time into spectral amplitudes as a function of frequency, space, and time. It has been used for a variety of applications including determination of layer thickness, stratigraphic visualization, reservoir characterization, and direct hydrocarbon detection. The commonly used spectral decomposition methods—such as STFT (short-time Fourier transform), CWT (continuous wavelet transform), and MPD (matching pursuit decomposition)—are linear in that they compute correlations between the signal and a family of time-frequency functions. Thus, they cannot achieve arbitrarily fine resolution in the time and frequency domain simultaneously due to the limitations imposed by the uncertainty principle (Qian, 2005).

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