Particle swarm optimization and least squares method for geophysical parameter inversion from magnetic anomalies data

The geophysical parameter inversion from magnetic anomalies data usually faces a non-linear optimization problem with multi-variable, multi-objective function extremum, multi-solution and so on. Therefore, it is necessary that the more stable and efficient algorithms is used in the geophysical inversion. Particle swarm optimization (PSO) has been used in the geophysical inversion. However, for high-dimensional, multi-peak function problems in magnetic anomalies data inversion, the effect using PSO method is not good, and it easy to fall into the local minimum. In this paper, we propose PSO and least squares method (LS) to solve magnetic anomalies data parameter optimized inversion. This method exploited to initialize non-linear parameter estimation using by PSO, and LS is used for accurate local search. We compare the results from PSO and proposed PSO-LS to invert the synthesized potential field. The results show that PSO-LS outperform PSO in terms of accuracy.