High-density resistivity method is widely used in engineering exploration because of its efficient and intuitive features. However, 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 inversion 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.
刘湘浩, 刘四新, 胡铭奇, 孙中秋, 王千. 基于OMAGA-BP算法的高密度电阻率法反演研究[J]. 物探与化探, 2023, 47(6): 1519-1527.
LIU Xiang-Hao, LIU Si-Xin, HU Ming-Qi, SUN Zhong-Qiu, WANG Qian. Research on inversion of high-density resistivity method based on OMAGA-BP algorithm. Geophysical and Geochemical Exploration, 2023, 47(6): 1519-1527.
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