3D inversion of gravity data of Safo mining site using linear programming and simplex algorithm

The aim of this research is to develop methods to derive sharp images of subsurface, which show the geometry of the structures in the region of interest. Structural inversion of gravity data -deriving robust images of the subsurface by delineating litho-type boundaries using density anomalies- is an important goal in a range of exploration settings (e.g., ore bodies, salt flanks). In this paper, an L1-norm based inversion approach is investigated. The density distribution of the subsurface is modeled on a uniform grid of cells. The model parameters are the density of each cell that is inverted by minimizing the L1-norm objective function using linear programming (LP) while satisfying a priori density constraints. LP method for structural inversion has two advantages: 1. It offers a natural way to incorporate a priori information regarding the model parameters. 2. It gives a subsurface image with sharp boundaries (structure). The inversion quality depends on a good priori estimation of the minimum depth of the anomalous body.