Abstarct: Due to the non-stationary property of seismic data, time-frequency transform has to be used to analyze them. During the last decade, spectral decomposition technique has proven to be an excellent tool to describe thin beds associated with channel sands, alluvial fans, and the like. However, the traditional spectral decomposition method baxsed on the short time Fourier transform, is difficult to acquire the accurate time-frequency spectrum for nonstationary seismic signals. Popular time-frequency methods have disadvantages. A good time resolution requires a short window and a good frequency resolution require a narrow-band filter, i.e. a long window, but unfortunately, these two cannot be simultaneously granted. The Wigner-Ville distribution (WVD) of a signal is the Fourier transform of the signals time-dependent autocorrelation function, which is a quadratic exxpression that is bilinear in the signal. As a result, cross-terms appear in locations of the resulting time-frequency spectra that either interferes with the interpretation of auto-terms or for which we can provide no physical interpretation. Due to the existence of cross-terms, WVD is little used. Reduction of the cross-terms is achieved by manipulating the ambiguity function as a mask that reduces the cross-terms while preserving the time and frequency resolution of the WVD. In this thesis, we propose a Deconvolutive Short-Time Fourier Transform (DSTFT) spectrogram method, which improves the time-frequency resolution and reduces the crossterms simultaneously by applying a 2-D deconvolution operation on the STFT spectrogram. Compared to the STFT spectrogram, the spectrogram obtained by the proposed method shows a significant improvement in the time-frequency resolution. we extracted timefrequency attributes, baxsed on the deconvolutive short time Fourier transform for identification of hydrocarbon reservoir. Results of this study on the synthetic and real seismic data examples illustrate the good performance of the DSTFT spectrogram compared with other traditional time frequency representations