Inversion of TEM measurement data via a quantum particle swarm optimization algorithm with the elite opposition-based learning strategy

• Published in: Computers & geosciences Vol. 174; p. 105334
• Main Authors: Jiao, Junjun, Cheng, Jiulong, Liu, Yuben, Yang, Haiyan, Tan, Dingrui, Cheng, Peng, Zhang, Yuqi, Jiang, Chenglin, Chen, Zhi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: algorithms; Bayesian theory; Elite opposition-based learning; Goaf; Inversion; premature birth; probability; Quantum particle swarm optimization algorithm; theoretical models; Transient electromagnetic method; Inversion; Elite opposition-based learning; Goaf; Quantum particle swarm optimization algorithm; Transient electromagnetic method
• ISSN: 0098-3004
• DOI: 10.1016/j.cageo.2023.105334

The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particle swarm optimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algorithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water-accumulated goaf. •Highlight 1: In view of the problems of premature convergence, slow convergence, and low calculation accuracy of the conventional particle swarm optimization (PSO) nonlinear inversion method, we introduce an elite opposition-based learning (EOL) strategy to optimize the quantum particle swarm optimization algorithm (QPSO). The improved PSO algorithm was named as EOL-QPSO algorithm.•Highlight 2: The EOL-QPSO algorithm inversion scheme was tested on the theoretical model and synthetic data containing noise to evaluate the accuracy and robustness of the inversion scheme, and then the inversion scheme was applied to the field measurement data inversion of TEM detection in coal mine water-accumulated goaf.•Highlight 3: The EOL-QPSO inversion scheme provides an excellent fit between the field TEM observation data and the forward data, and the EOL-QPSO inversion scheme more accurately reflects the electrical characteristics of the strata and the location and scope of the water-accumulated goaf.