Particle swarm optimization as an inversion tool for a nonlinear UXO model

The physics-based classification of unexploded ordnance (UXO) from electromagnetic induction (EMI) data is an exercise in nonlinear data inversion in which model parameters are extracted from a dataset by way of a physical model. Gradient-descent-based algorithms like Levenberg-Marquart (LM) suffer from local minima entrapment, but deliver rapid, accurate estimates when initialized near the global minima. Global optimizers, like particle swarm optimization (PSO) are less susceptible to this limitation. Here we consider an approach for parameter estimation based on a hybridization of PSO and Levenberg-Marquart. For purposes of initializing the LM procedure, we demonstrate that PSO applied to a reduced form of the physical model reaches a reliable initialization in one tenth the time of a coarse search of parameter space. Monte Carlo testing of the approach shows PSO initialized Levenberg-Marquardt estimation converging to the best-fit solution every time, as opposed to a randomly but reasonably initialized estimation. PSO as a stand alone approach provides less fit solutions on average than the PSO/Levenberg-Marquardt hybrid within the same computation time. We demonstrate these observations using both the simple, dipole model for the UXO problem as well as an enhanced form of this model capable of better capturing signal structure for large UXO-type objects.