Abstract: Time-frequency analysis is vital in seismic data processing, and the need for high-resolution technique intensifying as exploration advances. This paper traces the evolution of time-frequency methodologies beginning with the inherent limitations of traditional methods, particularly between temporal and spectral resolution. Recent progress is examined along two major fronts: fractional-domain analysis and compressive sensing. Fractional-order methods introduce fractional calculus to achieve more nuanced representation of seismic signals, thereby exposing their complex structural attributes compressive sensing theory uses signal sparsity algorithms optimization to enable efficient reconstruction and enhanced time-frequency characterization. In parallel, deep learning has emerged as a transformative tool, offering robust representation and generalization capabilities for sparse analysis, denoising, signal enhancement, and reservoir prediction. These advances mark significant breakthroughs in seismic exploration. This paper concludes with a synthesis of current methodologies and a perspective on future research directions, aiming to provide comprehensive reference for the continued development of seismic exploration technologies.