基于OMAGA-BP算法的高密度电阻率法反演研究

Research on inversion of high-density resistivity method based on OMAGA-BP algorithm

高密度电阻率法由于其高效、直观的特点,在工程勘查中得到广泛应用。然而,由于反演问题的高度非线性,传统的反演方法在刻画异常体边界时存在一定的不精确性。为了实现高精度的高密度电法二维非线性反演成像,克服BP算法损失函数参数空间存在大量鞍点影响计算精度,及普通遗传算法存在早熟收敛、难以赋予BP网络最优的权值阈值的问题,本文提出基于最佳保留策略的自适应遗传算法(optimum maintaining adaptive genetic algorithm,简称OMAGA)优化的BP神经网络进行高密度电法二维反演成像方法。该方法对仿真模型数据及实测数据的反演计算都得到了较好的结果,表明该方法具有泛化能力强、反演计算精度高的优点。该研究对未来高密度电阻率法的精确反演有一定的指导作用,有助于提高地下目标的识别精度。

High-density resistivity method is widely used in engineering exploration because of its efficient and intuitive features.How-ever,due to the high nonlinearity of the inversion problem,the traditional inversion method has some inaccuracy in describing the boundary of anomalous body.In order to achieve high precision two-dimensional nonlinear inversion imaging with high-density electrical method,to overcome the problem that a large number of saddle points in the parameter space of loss function of BP algorithm affect the calculation accuracy and that it is difficult to assign optimal weight threshold to BP network due to the precocious convergence of general genetic algorithm.In this paper,an Optimum Maintaining Adaptive Genetic Algorithm(OMAGA)is proposed to optimize the BP neural network for high density electrical two-dimensional inversion imaging.Good results have been obtained for the inversion calculation of simulation model data and measured data through this method,it shows that this method has strong generalization ability and high inver-sion accuracy.This study is helpful for the accurate inversion of high density resistivity method in the future,it is helpful to improve the accuracy of underground target identification.