Improving the S transform for high-resolution reservoir prediction and paleochannels delineation

Time frequency analysis of seismic data is a flexible and robust way for characterizing the subsurface properties with highresolution. There have existed a lot of time-frequency decomposition algorithms in the literature, among which the S transform based approaches still serves as one of the most widely used ways for time-frequency analysis because of its simple implementation, strong robustness, and high-fidelity delineation performance. It combines the separate strengths of the STFT and wavelet transforms with scale dependent resolution by using Gaussian windows, 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. We applied the slightly modified bi-Gaussian S transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to show the superior performance of the bi-Gaussian S transform in analyzing nonstationary signal components, hydrocarbon reservoir predictions, and paleochannels delineations with an obviously higher resolution.