Imaging thin beds using attributes achieved in spectral decomposition by short time Fourier transforms

One of the most fundamental reservoir characteristics is the thickness. The analysis of thin bed tuning on seismic reflectivity has been studied extensively by Wides (1973) and Neidel and Pogiaggliomi (1977), who discussed the limits of seismic resolution. During the past decade, the industry has developed a plethora of new attributes in studying thin beds by employing spectral decomposition (Peyton et al., 1998; Partyka et al., 1999), and attributes which are obtained from it (Marfurt and Kirlin, 2001). Spectral decomposition refers to all methods that generate frequency spectrums consisting of amplitude spectrum, phase spectrum, change of phase with frequency and power spectrum in windows with the center of each time sample of a trace. These methods are used in studying geological features, thin beds, hydrocarbon reservoirs and noise attenuation. The most important of these methods are short time Fourier transform (STFT), continuous wavelet transforms, S-transform, Wigner-Ville distribution and matching pursuit decomposition. The result of a trace spectral decomposition is a time-frequency map.