Abstract: Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis. However, the application is problematic since different wavelets always return the same sequence analysis results. To address this issue, we applied five commonly used wavelets to theoretical sequence models to document some application criteria. Five gradual scale-change sequence models were simplified from the glutenite succession deposition by gravity flows to form the fining-upwards cycle sequences (FUCS) and coarsening-upwards cycle sequences (CUCS). After conducting theoretical sequence model tests, the optimal wavelet (sym4) was selected and successfully used with actual data to identify the sequence boundaries. We also proposed a new method to optimize the scale of continuous wavelet transformation (CWT) for sequence boundary determination. We found that the balloon-like marks in scalograms of db4, sym4, and coif4 wavelet determine, respectively, the fourth-order sequence boundary, the thick succession sequence boundaries in FUCS, and the thick succession sequence in FUCS and CUCS. Comparing the sequence identification results shows that the asymmetric wavelets had an advantage in high-frequency sequence boundary determination and sedimentary cycle discrimination through the amplitude trend of the coefficient, in which the sym4 wavelet is the most effective. In conclusion, the asymmetry of wavelets is the first selection principle, of which asymmetric wavelets are more sensitive to sediment deposition by flood flows. The match of the wavelet between the sequence is the second selection principle, in which the correlation of time-frequency impacts the accuracy of sequence surface localization. However, the waveform of the wavelet is a visual and abstract parameter for sequence boundary detection. The appropriate wavelet for lacustrine sequence analysis is the asymmetric wavelet with a weak number of side lobes. The depositional flows, depositional process, and autogenic are three sedimentary factors that influence the sequence analysis results.