During an earthquake, the energy released by vertical crustal movement propagates to ionospheric altitudes in the form of acoustic or gravity waves, triggering co-seismic ionospheric disturbances （CID）. Studying CIDs holds significant practical value, including enhancing earthquake early warning capabilities, deepening the understanding of lithosphere-atmosphere-ionosphere coupling mechanisms, and comprehensively assessing the scope and severity of seismic hazards. GNSS-TEC （Global Navigation Satellite System-total electron content）is one of the key observational methods for investigating seismic ionospheric effects. Influenced by seismic parameters （e.g., magnitude, focal mechanism） and non-seismic factors （e.g., epicentral topography, solar activity, geomagnetic activity, atmospheric wind fields）, CID-induced TEC disturbances （CID-TEC） exhibit notable variability and diversity in their disturbance patterns, morphology, and time-frequency characteristics. Accurate classification of CID-TEC is fundamental to precisely analyzing disturbance propagation velocity, direction, and source localization. However, under conditions of sparse observational data, data sparsity can prevent the natural formation of a linear time-epicentral distance relationship in travel-time diagrams when fitting CID propagation speeds. Additionally, the fitting process may suffer from the omission of similar disturbances or contamination by dissimilar disturbances, leading to deviations between the fitted and actual propagation velocities—or even generating spurious velocities in extreme cases.

Based on the differential characteristics of CID-TEC, this paper proposes a CID-TEC differentiation and analysis method combining cross-correlation and continuous wavelet transform. Specifically, cross-correlation analysis quantifies the degree of differentiation between disturbances, while wavelet analysis characterizes the time-frequency properties of CID-TEC, enabling their differentiation and classification. The method is validated through three case studies. Results show that the cross-correlation coefficients between CID-TEC signals of the same propagation mode are significantly higher than those between different modes, indicating that cross-correlation analysis can quantitatively measure differentiation and qualitatively determine whether disturbances share the same propagation mode. The time-frequency features provided by continuous wavelet transform further depict the energy distribution of CID-TEC in the time and frequency domains, identifying the acoustic-gravity wave frequency range responsible for exciting CID-TEC.

Applying this method, we investigate the CIDs triggered by the M_W7.2 Alaska earthquake on July 16, 2023. After differentiation analysis and classification, spurious propagation modes caused by spatiotemporal （epicentral distance） overlaps of partial CID-TEC signals are eliminated. Three acoustic wave propagation modes are identified, with corresponding velocities of 741.85 m/s, 690.29 m/s, and 680.38 m/s, and the potential dominant propagation directions of CID-TEC are determined.