Inversion of Self-potential Generated from Biogeobattery Model by Particle Filter Based on Particle Swarm Optimization Algorithm

Abstract

Organic pollution has been exacerbated by industrial activity. How to quickly and effectively delineate the pollutant distribution and carry out targeted treatment has become an urgent matter. In order to improve the reliability of observation data inversion and interpretation, we propose a new particle filter algorithm based on particle swarm optimization, which does not only improves the accuracy of the particle filter algorithm but also inherits the advantages of fast convergence speed of particle swarm optimization algorithm. This method is tested and verified in a numerical model and sandbox experiments. The results show that this method can accurately estimate the parameters of anomaly sources and has good anti-noise ability. It is suitable for the inversion of pollution monitoring data which are easily disturbed by noise.