Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

基于粒子群算法的简单极化体自然电场反演

Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.

本文采用粒子群算法反演了简单极化体引起的自然电场异常。首先,引入标准粒子群算法对垂直半无限延伸柱状体的模型参数进行了反演。然后在此基础上讨论了粒子群的搜索行为,研究了粒子群在迭代过程中的位置变化情况。不同粒子行为的存在使粒子群能够更全面地探索整个解空间,保证了粒子群算法可在反演自然电位异常方面取得显著效果。最后,在标准粒子群算法的基础上引入了6种通过改变粒子位置、惯性权值和学习因子等因素的更新策略来提高反演精度或收敛速度的改进粒子群算法,并通过模型反演验证了各算法的有效性。本文通过数值实验对标准粒子群算法及各改进粒子群算法的勘探性与开发性做了讨论,对于在规则极化体自然电场反演中如何确定有效反演策略具有一定的指导意义。