Non-linear stochastic inversion of regional Bouguer anomalies by means of Particle Swarm Optimization: Application to the Zagros Mountains

Estimating the lateral depth variations of the Earth’s crust from gravity data is a non-linear ill-posed problem. The ill-posedness of the problem is due to the presence of noise in the data, and also the non-uniqueness of the problem. Particle Swarm Optimization (PSO) is a stochastic population-based optimizer, originally inspired by the social behavior of fish schools and bird flocks. PSO is a global search method, meaning that it has the ability to escape local minima. In addition, PSO is an iterative method, wherein an initial solution is chosen randomly and then improved iteratively until the algorithm finds a solution close enough to the global minimum. Herein, the inverse problem of estimating the thickness of the crust from gravity anomalies is formulated as a single objective optimization problem and is solved by PSO. The method is first tested on a realistic synthetic crustal model both with and without the presence of white Gaussian noise (WGN). Then it is applied to the gravity data from EIGEN-6c4, the latest combined global gravity model, in order to find the base of the crust in the Zagros Mountains (Iran) and compare the results with those of other geophysical methods. The assumed crustal model is one with a linear density gradient in which the densities at both the surface and the base of the crust are fixed. Results agree well with the previously published works including both seismic and potential field studies.