Seismic Time-frequency Analysis Using Bi-Gaussian S Transform

Time frequency analysis of seismic data is a flexible and robust way for characterizing the sub-surface properties with high resolution. There have existed a lot of time-frequency decomposition algorithms in the literature, among which the S transform based approaches still serve as one of the most widely used ways for time-frequency analysis because of its simple implementation, strong robustness, and highfidelity delineation performance. It combines strengths of the short-time Fourier transform (STFT) and wavelet transform with scale dependent resolution by using Gaussian windows, with window widths scaled inversely with frequency. One problem with the use of traditional symmetric Gaussian window is degradation of time resolution in the time-frequency spectrum due to the long front taper. In this abstract, we study the performance of an improved S transform with a bi-Gaussian window used to construct asymmetry bi-Gaussian windows. The asymmetry bi-Gaussian can obtain an increased time resolution in the front direction. This increased time resolution will result in a high-resolution event picking and a significantly improved time-frequency characterization for oil&gas traps prediction.