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Parallel Inversion of 1D Magnetotelluric Data Using Particle Swarm Optimization Algorithm

机译:基于粒子群算法的一维大地电磁数据并行反演

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The inversion of magnetotelluric data is a multi-parameter,nonlinear,and multimodal optimization problem.Particle swarm optimization (PSO) algorithm,which was developed by enlightenment of the behavior of birds in looking for food,is the fine solver for this geophysical inversion problem.As the forward problem becomes complex,the inversion becomes much more slow.We proposed a parallel PSO inversion algorithm to speedup the inversion of 1D magnetotelluric data.The numerical results show that the parallel PSO algorithm can speedup the inversion effectively and ensure the solution accuracy.
机译:大地电磁数据的反演是一个多参数,非线性,多峰的优化问题。粒子群优化算法(PSO)是针对鸟类寻找食物行为的启发而开发的,是解决地球物理反演问题的很好的解决方案。随着正向问题的复杂化,反演变得越来越慢。我们提出了一种并行PSO反演算法来加速一维大地电磁数据的反演。数值结果表明,并行PSO算法可以有效地加速反演并确保求解精度。 。

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