Adaptive Generalized S-Transform With a Chirp-Modulated Window

Accurate time–frequency representation (TFR) is essential for resolving spectral components in seismic signal analysis and enhancing geological interpretation. In this study, we present an enhanced (a new) generalized S-transform (GST) that integrates a chirp-modulated window, improving time–frequency (TF) localization, reducing spectral smearing, and increasing resolution. Furthermore, a multiobjective optimization (MOO) strategy is employed to tune the GST parameters, effectively distinguishing transient and harmonic components within the signal. These advancements enhance the adaptive characteristics of GST, optimizing the frequency-dependent windowing function for more effective feature extraction. To validate its effectiveness, the proposed method is applied to both synthetic and real seismic data, employing a frequency-weighted Red-Green-Blue (RGB) mapping approach based on raised cosine basis functions for visualization. Comparative analysis with conventional TFR methods—standard S-transform (ST), general linear chirplet transform (GLCT), and adaptive GST (AGST)—demonstrates that our approach achieves higher resolution and clearer geological feature delineation.