Adaptive Generalized S-Transform With a Chirp-Modulated Window
Abtin Pegah, Bo Feng, Amin Roshandel Kahoo, Huazhong Wang, Ehsan Pegah · IEEE Transactions on Geoscience and Remote Sensing · 2025
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 localization, reducing spectral smearing, and increasing resolution. Furthermore, a multi-objective optimization 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 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 Generalized S-Transform (AGST)—demonstrates that our approach achieves higher resolution and clearer geological feature delineation.