Lunar Geophysical Parameter Inversion with Admixed Particle Swarm Optimization ADPSO

Ordinary particle swarm optimization PSO fails frequently in estimation of low sensitivity selenophysics parameters. With an adaptive inertia weight and a mutation factor, an admixed particle swarm optimization ADPSO is proposed. It is found that misfit is not reduced as increasing number of particles and probability of mutation. For four-parameter inversion, the best-fitting parameters can be estimated as considering about 400 particles and a low probability ( < 0.03) of mutation. With employment of gravity filed model GL0990d and altimeter data from LRO, we apply the method into selenophysical parameter inversion on southern highland of the moon. It shows a best-fit between modeled admittance spectral and observed values. Small residual of gravity anomaly verifies success of parameter inversion and validity of the method. The approach can be used in selenophysics research and could provide a reference for large-scale estimation of parameters.