Surface waves are a prevalent form of coherent interference in seismic recordings, characterized by low frequency, high energy, and slow velocity, which can significantly affect seismic data processing. Currently, surface wave suppression is primarily achieved by leveraging the differences between surface waves and signal waves in the frequency, amplitude, and wavenumber domains. Among these methods, frequency–wavenumber (FK) filtering is widely used. However, it requires manual selection of regions during filtering, which becomes challenging when processing large volumes of seismic data. Therefore, adaptive surface-wave suppression methods are essential for practical data processing. FK filtering transforms data into the FK domain to suppress linear noise. However, because surface waves are dispersive and exhibit partial nonlinearity, FK filtering alone is insufficient to fully suppress them. To address these issues, this study introduces a temporal dimension into the FK domain, leading to the development of the time–frequency–wavenumber (TFK) transformation. This transformation further separates surface and signal waves in the time domain, thereby mitigating dispersion. Moreover, it facilitates adaptive filtering of seismic data across different time intervals in the FK domain. By cross-correlating FK data from different time periods, adaptive filters were derived for each interval, which were then applied to obtain filtered seismic records. Comparisons between synthetic and real-world data demonstrated that our approach effectively suppresses surface waves while preserving the relative amplitude characteristics of signal waves.