Abstract: Time-frequency analysis is a key technology in seismic data processing, and with the deepening of seismic exploration, the demand for high-resolution time-frequency analysis is becoming increasingly urgent. This paper reviews the development process of time-frequency analysis methods in recent years, and first explores the limitations of traditional methods, such as the trade-off between time resolution and frequency resolution. Subsequently, the latest developments in time-frequency analysis are elaborated in detail from two dimensions: fractional domain and compressive sensing. Fractional-order time-frequency analysis provides a more refined time-frequency representation by introducing the concept of fractional order, which helps to reveal the complex characteristics of seismic signals. The compressive sensing theory utilizes the sparsity of signals and optimizes algorithms to achieve signal reconstruction and time-frequency representation. In addition, the paper also elaborates on the research status, advantages, and shortcomings of deep learning-based time-frequency analysis of seismic signals. Deep learning methods have demonstrated strong representation capabilities and generalization performance in sparse time-frequency analysis, denoising, signal enhancement, and reservoir prediction, bringing new breakthroughs to the field of seismic exploration. Finally, this paper summarizes the time-frequency analysis methods in the current field of seismic exploration and looks forward to future development directions, in order to provide valuable references for promoting the development of seismic exploration technology.