Abstarct: To analyze a signal, it is important to obtain time-varying information, such as instantaneous frequency (IF) and instantaneous amplitude (IA). Time- frequency (TF) spectral analysis (TFA) is an effective tool for describing the time-varying characteristics of nonstationary signals. A seismic signal is a non-stationary signal, which means that its frequency content changes with time. Therefore, in order to accurately check the time position of the seismic signal frequency content, it is necessary to perform a time-frequency analysis on it. In order to achieve improved time and frequency resolution, various time-frequency analysis methods have been introduced over many years, such as, short-time Fourier transform (STFT), Continuous wavelet transform (CWT), S transform, Wigner-Weil distribution ( WVD) and Empirical Mode Analysis (EMD). Also, post-processing transformations have been developed that are able to increase the resolution of time-frequency representations obtained from conventional methods, including Synchrosqueesing transform (SST) and Reassignment method (RM). Synchroextracting transform (SET), which is a post-processing process of the short-time Fourier transform, is a new technique in the field of achieving time-frequency representation with improved resolution.Synchroextracting transform can produce more focused energy for time-frequency representation and also allows signal reconstruction. The main idea of SET is inspired by the synchrosqueesing transform method and ideal time-frequency representation theory, which preserves the time-frequency information obtained from the short-time Fourier transform, which is related to the time-varying characteristics of the signal, and the extra dissipated energy in removes the time-frequency screen.In this thesis, the intention is to use the SET method in time-frequency analysis of seismic data. Time-frequency analysis of seismic data has different analytical and interpretative applications including seismic denoising, deconvolution, hydrocarbon detection and channel detection.It's obvious reaching a time-frequency presentation with a better resolution will results in these applications. In this thesis, the performance of SET method in presenting time-frequency representation of synthetic signals has been investigated. Also, this method has been used in interpretative applications of low-frequency shadow detection, constant quality factor estimation, and AVO analysis in the time-frequency domain. The results show the acceptable performance of the SET method in these applications.