PSBP在高密度电阻率法二维反演中的应用

Application of PSBP method in high-density two-dimensional resistivity inversion

针对神经网络算法在非线性反演中容易陷入局部极小、收敛慢、反演精度差等问题,提出了将粒子群优化(Particle swarm optimization)算法与BP神经网络(Back propagation neural networks)进行混合反演(简称PSBP)。最后,通过经典的地电模型对本文方法的有效性进行验证,结果表明,本文方法与线性反演方法、BP神经网络反演方法对比,具有明显的优势,并取得了很好的反演结果。

In nonlinear inversion,neural network algorithm has the shortcomings of easy to fall into local minimum,slow convergence and poor inversion accuracy.To overcome these shortcomings,the Particle Swarm Optimization（PSO）is combined with Back Propagation（BP）neural network to achieve inversion（referred as PSBP）.The effectiveness of the PSBP is verified by the classical geoelectric model.Compared with the linear inversion and BP neural network inversion methods,the proposed PSBP method has obvious advantages with good inversion result.