Abstarct: The non-elastic propagation of the energy into the earth lead to change the frequency content of the seismic signal. Hence, time or frequency representations separately do not provide required information of signal in processing and interpretation of seismic data, such as noise attenuation, low frequency shadow identification associated with gas reservoirs, and other seismic attributes. Simultaneous representation of time and frequency information (time-frequency representation) helps us to better understand the changes in the non-stationary signal. Therefore, the achievement and development of such transforms that can provide time-frequency representation, increasingly necessary. Short-time Fourier transform, wavelet transform, Wigner-Ville distribution and S transform are the common examples of such transforms which usually used in most fields of sciences like seismology. An accurate time-frequency representation is important for highlighting subtle geologic structures and detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, beacause of smoothing energy in their window. Synchrosqueezing wavelet transform is a new version of the wavelet transform which incorporated features of empirical mode decomposition and frequency reassignment methodes. It provides a high-resolution time-frequency representation allowing the identification of instantaneous frequencies in seismic signals to highlight individual components. In this thesis, synchrosqueezing wavelet transform have been introduced and applied on seismic data for denoising, AVO analysis in time-frequency domain and detection of low frequency shadow associated with gas reserviors. Comparison of the obtained results by synchrosqueezing wavelet and traditional time-frequency transforms, show that the synchrosqueezing wavelet transform is a good choice for replacement.