A Bayesian Approach for Full-waveform Inversion Using Wide-aperture Seismic Data

Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution velocity models, which is based on the wave equation. We investigate the frequency-domain FWI of wide-aperture data. We have used a Bayesian inversion framework with l-BGFS algorithm. For the prior information, we have used a spatial covariance operator based on information collected in two wells at the ends of the velocity model. The data uncertainties were estimated according to the distance source-receiver (offset) and the angular frequency to emphasizes the waves with a greater angular range (diving waves). Finally, we report a numerical example using the Marmousi model with a maximum offset of 16,960 meters to demonstrate the effectiveness of the proposed inversion methodology. The proposed strategy has been successful to obtain gas and oil cap structures in high-resolution.