Pre-stack wavelet estimation for broadband data

Pre-stack elastic inversion requires a reliable wavelet model. This wavelet model must take into account the angle dependency of the wavelet, as well as the time dependency. This is even more important for broadband data, as the wavelets exhibit more variations, due to the fact that the maximum frequency is very high for small time and angles, but much smaller for large time and angle. We show how a continuously varying wavelet model can be estimated through a Bayesian inversion. This wavelet model can be used to pre-process the gathers and provide a zero-phase wavelet model for pre-stack elatic inversion. Synthetic and real data examples are shown to support this.