Joint Inversion of Gravity Gradient Tensor Data Based on L1 and L2 Norms

Gravity gradient data are sensitive to local density anomalies in regional geological structures. Compared with the inversion of one component of the gravity field, joint inversion of the gravity gradient tensor data can provide more constraint information, reduce the nonuniqueness, and improve the reliability of inversions. This study developed a joint inversion of gravity gradient tensor data based on the coordinate descent algorithm (CDA). A model function based on mixed $L1$ and $L2$ norms is used to constrain the joint inversion. The synthetic model tests show that the proposed method can effectively image the location of density anomalies with proper amplitude. Finally, this joint inversion method is applied to the gravity gradient tensor data observed over the Vinton Dome, Calcasieu Parish, LA, USA. The results reveal a high-density caprock consistent with the geological information, proving the ability of this method to process the actual data.