Inversion of self-potential source based on particle swarm optimization

Particle swarm optimization is widely applied in self-potential inversion, but most of these applications are limited to simple polarized bodies like inclined sheets and spheres. In this paper, two variants of particle swarm optimization are formed by introducing the resistivity constraint matrix and the damping factor and are then applied to two-dimensional self-potential source inversion. The two variants are combined to balance the exploration and exploitation capabilities in different situations, and the rationality of the parameter selection is validated through the stability analysis. To ensure both compactness and smoothness of the inversion results, we adopt an objective function based on L1-L2 norm regularization. Finally, we verify the effectiveness of the proposed algorithm through synthetic examples, the Cu-Fe model sandbox experiment and a field example. By imposing certain constraints, particle swarm optimization also shows great potential in high-dimensional self-potential source inversion, instead of being limited to the inversion of a few parameters.