Abstarct: Due to the non-stationary property of seismic data, spectral decomposition baxsed on Fourier transform cannot reveal the appropriate characteristics of them. Since, spectral components of a non-stationary signal are functions of time, so a simultaneous represen-tation of time and frequency will be very useful for the analysis of such signals. Time-frequency transform upgraded spectral decomposition to a new step and can show time and frequency information simultaneously. Spectral analysis of seismic data using time – frequency transforms, converts the seismic amplitudes which are a function of space and time to spectral values, which are a function of frequency, time and space. Nowadays, the time - frequency transforms have been widely used in the seismic data processing and interpretation. They can be used in estimation of laxyer thickness, reservoir character-ization and exploration, estimation of absorption coefficient, burial channel detection, random and coherent noise attenuation and, etc. In this paper, we compute the time-frequency representation of a signal using con-strained least squares inversion method. The time - frequency resolution of this method is higher than conventional methods. We used the mentioned algorithm to illuminate the low-frequency shadow corresponding to a gas reservoir at one of the gas fields in the South West of Iran. The results of the real data example show that the constrained least squares inversion method has a much better resolution than the conventional spectral de-composition and can potentially be used to detect hydrocarbons from a gas reservoir di-rectly using low frequency shadows