The generalized Beta wavelets for fluvial channels delineation of seismic data

Time-frequency analysis of reflection seismic data can provide significant information to delineate the subsurface fluvial channels. Based on the convolution model of reflection seismology, the continuous analytic wavelet transform (AWT) of a seismic signal requires that the mother wavelet can be match to the real seismic waveform. Therefore, we propose a new analytic wavelet family, which we refer to the generalized Beta wavelets (GBWs). By varying two parameters controlling the wavelet shapes, the time-frequency representation of GBWs can be given enough flexibility while remaining exactly analytic. For an adaptive trade-off of the time-frequency representation, a data-driven workflow is designed to optimize suitable parameters of GBWs in the seismic time-frequency analysis. Finally, we apply AWT with GBWs on 3D seismic data to show its potential to discriminate stacked fluvial channels in vertical sections and to delineate more distinct fluvial channels in the horizon slices.