One-dimensional inversion of induced polarization sounding data based on the differential evolution algorithm with two-step mutation
DING Zhi-Jun1(), LUO Wei-Bin2(), LIAN Wei-Zhang1, ZHANG Xing1, HE Hai-Pin2
1. Gansu Nonferrous Geological Survey Institute, Lanzhou 730000, China 2. Lanzhou Resources and Environment Vocational and Technical University, College of Geology and Jewelry, Lanzhou 730021, China
The one-dimensional inversion of induced polarization (IP) sounding data involves multi-parameter nonlinear optimization. This study achieved the one-dimensional (1D) inversion of IP sounding data based on the improved global optimization algorithm of differential evolution (DE) with two-step mutation. The conventional DE algorithm includes mutation (single-step), crossover, and selection operations. The two-step mutation method proposed in this study can produce new individuals through the mutation of the optimal individual and two randomly selected individuals in steps, thus enhancing the influence of the optimal individual and the global optimization ability. The model test results show that the two-step mutation method has a higher optimization ability than the conventional method. Specifically, the polarizability parameters were loaded using the equivalent resistivity method, and the surface IP sounding resistivity curves of a layered model can be quickly calculated through forward modeling using the digital filtering algorithm. Based on this, the DE algorithm with two-step mutation was employed to produce new individuals through continuous mutation. Then, the resistivity obtained through forward modeling was fitted with the observed values, and the individuals whose fitness approached the maximum fitness were selected as the inversion results. The inversion method proposed in this study features simple operations and fast calculations. As verified through the calculations of H- and KH-type geoelectric models, the inversion method enjoys high fitting accuracy.
丁志军, 罗维斌, 连伟章, 张星, 何海颦. 基于两步变异差分进化算法的激电测深一维反演[J]. 物探与化探, 2023, 47(4): 1033-1039.
DING Zhi-Jun, LUO Wei-Bin, LIAN Wei-Zhang, ZHANG Xing, HE Hai-Pin. One-dimensional inversion of induced polarization sounding data based on the differential evolution algorithm with two-step mutation. Geophysical and Geochemical Exploration, 2023, 47(4): 1033-1039.
Liu H F, Liu J X, Liu R, et al. Research proqress of induced polarization method in nonferrous metal mineral exploration[J]. The Chinese Journal of Nonferrous Metals, 2023, 33(1):203-222.
Chen R J, Liu C M, He L F, et al. Key technology research and application of array spread spectrum induced polarization method[R]. China Nonferrous Metals Industry Association, 2020.
Pan B D. Research on fine inversion method of induced polarization method in Dongwuqi Area,Inner Mongolia[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2022, 44 (4):459-467
Shi X M, Wang J Y. Lecture on nonlinear inversion methods of geophysical data (IV)—Genetic algorithm[J]. Journal of Engineering Geophysics, 2008, 5(2):129-140
doi: 10.1088/1742-2132/5/2/001
Liu Y. Resistivity and polarizability inversion of pollution sites based on convolutional neural network[D]. Shanghai: Shanghai Institute of Technology, 2021.
Wang T Y, Hou Z, He Y X, et al. Magnetotelluric inversion based on the improved differential evolution algorithm[J]. Progress in Geophysics, 2022, 37(4):1605-1612.
[10]
Mykel J K, Tim A W. Algorithms for optimization[M]. Cambridge: The MIT Press, 2019.
Gao Y, Xia B, Zhang L Y, et al. Differential evolution cooperative localization algorithm[J/OL]. Radio Engineering, http://kns.cnki.net/kcms/detail//13.1097.TN.202301117.1402.002.html
Ding X Z, Li Z X, Li Y G, et al. Calibration of magnetic gradient tensor system with differential evolution algorithm[J]. Chinese Journal of Geophysics, 2022, 65(12):4930-4943.
Liao Z W. Simultaneously locating multiple roots of nonlinear equations based on differential evolution[D]. Wuhan: China University of Geosciences(Wuhan), 2019.
Wang Z J, Liu Y, Wu X T, et al. A calculation of apparent resistivity with device coefficient in 1D resistivity sounding[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2022, 44 (5):590-596.
[16]
Ghosh D P. Inverse filter coefficients for the computation of apparent resistivity standard curves for a horizontally stratified earth[J]. Geophysical Prospecting, 1971, 19(4):769-775.
doi: 10.1111/gpr.1971.19.issue-4
[17]
Guptasarma D. Optimization of short digital linear filters for increased accuracy[J]. Geophysical Prospecting, 1982, 30(4):501-504.
doi: 10.1111/gpr.1982.30.issue-4
Deng J, Wei W H, Zhang Y H, et al. Adaptive differential evolution algorithm based on three population evolution strategy[J]. Journal of Dongguan University of Technology, 2022, 29(1):60-66,76.