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 (p...

This paper presents a review of spectral decomposition of seismic data, since it's inception nearly in 1997. It discusses various techniques and applications of spectral decomposition in seismic data processing and interpretation. It is also known as timefrequency analysis consists of transforming non stationary signal in time/space from time/space domain to time/space vs frequency domain. The ...

Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews...

spectral decomposition or time-frequency representation (tfr) is a powerful tool for analyzing time varying nature of seismic data. mapping of a one dimensional seismic time trace to a two dimensional function of time and frequency reveals some characteristics of seismic signals that are not available in only time or frequency representations. this approach has been widely used in seismic explo...

Currently, the automatic wheezes detection methods are based on the identification of a particular shape of peaks in the respiratory power spectrum. These techniques present a high false detection rate, caused by the presence of peaks in normal sounds similar to those characterising wheezes. In this paper, a new method for automatic wheezes detection based on the wavelet packets was developed. ...