Reconstruction of Quantitative Variable Density Acoustic Reflectivity in the Context of Velocity Model Building

The quality of focusing panels (Common Image Gathers) plays a fundamental role in the construction of the macro-model via image domain techniques. Recent works demonstrated that iterative least-squares migration is recommended for obtaining reliable focusing panels: this ensures relevant tomographic macro-velocity updates. In practice, iterative least-squares migration needs to be accelerated through suitable pre-conditioners such as pseudo-inverses of the forward modelling operator. The pseudo-inverses are currently limited to the constant density acoustic case. In this paper, we first discuss the impact of density variations on focusing panels, and then propose an approach to quantitatively reconstruct two acoustic parameters. The main ingredient is the Radon transform. From an extended reflectivity (single iteration), we apply the Radon transform to reconstruct the inverse of the bulk modulus and the density perturbations in the physical domain, while preserving the data fit. We validate our approach on the Marmousi-II dataset, demonstrating that the proposed approach is an efficient alternative to the more expensive least-squares migration. As expected, there is a leakage between the inverted two parameters.