Abstract. As one of the most active nonlinear inversion methods in transient electromagnetic (TEM) inversion, the back propagation (BP) neural network has high efficiency because the complicated forward model calculation is unnecessary in iteration. The global optimization ability of the particle swarm optimization (PSO) is adopted for amending the BP's sensitivity to its initial parameters, which avoids it falling into a local optimum. A chaotic-oscillation inertia weight PSO (COPSO) is proposed for accelerating convergence. The COPSO-BP algorithm performance is validated by two typical testing functions, two geoelectric models inversions and a field example. The results show that the COPSO-BP method is more accurate, stable and needs relatively less training time. The proposed algorithm has a higher fitting degree for the data inversion, and it is feasible to use it in geophysical inverse applications.

中文翻译：

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使用反向传播神经网络和改进的粒子群优化快速逼近瞬态电磁数据的一维反演

摘要。作为瞬态电磁（TEM）反演中最活跃的非线性反演方法之一，反向传播（BP）神经网络由于在迭代中不需要复杂的正演模型计算而具有很高的效率。利用粒子群优化（PSO）的全局优化能力来修正BP对其初始参数的敏感性，避免陷入局部最优。提出了一种混沌振荡惯性权重 PSO (COPSO) 来加速收敛。COPSO-BP 算法的性能通过两个典型的测试函数、两个地电模型反演和一个现场实例进行验证。结果表明，COPSO-BP 方法更准确、更稳定，所需的训练时间相对较少。所提算法对数据反演具有较高的拟合度，