Adaptive Peak Extracting Transform and Its Application in Sedimentary Cycle Division

Sedimentary cycle division is a critical task within sequence stratigraphy and provides significant guidance value in the exploration of gas resources. For the division of sedimentary cycles by analyzing the time-frequency (TF) curves of seismic data, the key lies in high-resolution highlighting of changes in dominant frequency induced by variations in layer thickness. To improve the energy concentration of TF analysis (TFA) methods, this article proposes an adaptive peak extracting transform (APET) for characterizing sedimentary cycle patterns. In this APET, we first establish a local peak criterion within the short-time Fourier transform (STFT) framework to obtain instantaneous frequency (IF) trajectories. Subsequently, a time-varying window function is constructed by Rényi entropy to determine the optimal window length at each moment. Finally, the TF coefficients are reallocated to the retrieved IF within the optimal window. The local peak criterion and the time-varying window function effectively enhance the TF resolution. The synthesized signals verify that the proposed APET exhibits highly focused TF representation (TFR). In addition, we apply APET to several typical sedimentary cycle models and field seismic data, indicating that the proposed method is a potential tool for sedimentary cycle analysis.