Inversion of Gravity Data by Constrained Nonlinear Optimization based on nonlinear Programming Techniques for Mapping Bedrock Topography

A constrained nonlinear optimization method based on nonlinear programming techniques has been applied to map geometry of bedrock of sedimentary basins by inversion of gravity anomaly data. In the inversion, the applying model is a 2-D model that is composed of a set of juxtaposed prisms whose lower depths have been considered as unknown model parameters. The applied inversion method is a nonlinear one, which minimizes the objective functions by definition of different objective functions and an initial simple model to improve the initial model parameters. In this study, for different cases, sufficient objective functions are defined based on the condition which is encountered in the inverse problem. To control the under- determinacy part of the inverse problem and to prevent unreasonable instability in the resultant model, damping terms are added to the objective function. The act of synthetic inversion for different cases of parameterization has been examined and the results are analyzed. The results have almost depicted the recovery of the model and also fitting of the original and model response data. In addition, the method has been used to invert real gravity data in Aman Abad area. From the inversion results, depths of the basin, features like fractures and uplift in bedrock, along specific profiles have been determined. Thicker parts of sediments in the basin along the profiles have also been recognized, which have the potential for exploring drinking water in this area.