Joint inversion of gravity and gravity gradient data using smoothed L0 norm regularization algorithm with sensitivity matrix compression - Niu, Tingting; Zhang, Gang; Zhang, Mengting; Zhang, Guibin (Frontiers in Earth Science, 2023)
Improving the efficiency and accuracy are critical issues in geophysical inversion. In this study, a new algorithm is proposed for the joint inversion of gravity and gravity gradient data. Based on the regularization theory, the objective function is constructed using smoothed L0 norm (SL0), then the optimal solution is obtained by the nonlinear conjugate gradient method. Numerical modeling shows that our algorithm is much more efficient than the conventional SL0 based on the sparse theory, especially when inverting large scale data, and also has better antinoise performance, while preserves its advantage of high accuracy. Compressing the sensitivity matrices has further improved the efficiency, introducing the data weighting and self-adaptive regularization parameter have improved the convergence rate of the inversion. Moreover, the impacts of the depth weighting, model weighting and density constraint are also analyzed. Finally, our algorithm is applied on the gravity and gravity gradient measurements at the Vinton salt dome. The inverted distribution range, thickness and geometry of the cap rock are in good agreement with previous studies based on geological data, drilling data, seismic data, etc., validating the feasibility of this algorithm in actual geological condition.