Searching the Parameter Space for Resolution and Uniqueness in Elastic Anisotropic Waveform Inversion

Full waveform inversion (FWI) can retrieve high-resolution subsurface medium parameters from the observed data. However, the inverse problem is typically ill-posed and non-unique, especially for the multi-parameter elastic FWI (EFWI) in complex media. Besides, high-resolution EFWI is computationally expensive because it requires fine discretization for the whole computational domain. The redatuming approach allows us retrieve the virtual data at the target level using mainly a kinematically accurate overburden, thus, focusing the high-resolution inversion on the target zone to reduce the computational cost. In multi-parameter inversion, even at the target zone, we will need to utilize a prior information and we do that through deep learning to find the connection between well information and the a prior needed by FWI. In such a framework, we take into consideration the proper parameter makeup for reducing the ill posedness of the problem. Numerical tests on the synthetic SEAM model are used to demonstrate the performance of the proposed inversion scheme, and its robustness in the multi parameter inversion case.