Permittivity Estimation With Adaptive Genetic Algorithm and Its Application in the Detection of Lava Tubes

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 12
• Main Authors: Kuan Wong, Hon, Xu, Yi, Wang, Bangbing, Chen, Rui, Meng, Xindong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Adaptive inversion; Algorithms; Confidence intervals; Convergence; Convolution; Curve fitting; Exploration; genetic algorithm (GA); Genetic algorithms; Genetics; Ground penetrating radar; ground-penetrating radar (GPR); Impedance; Lava; Mathematical models; Parameter estimation; Permittivity; Physical properties; Planetary evolution; Polar exploration; Radar; Radar antennas; Radar data; Radar detection; Space exploration; Statistical analysis; Statistical models; Tubes
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3494017

Ground-penetrating radar (GPR) is one of the powerful tools to reveal the subsurface structure of planetary bodies and has obtained great success in planetary exploration. Permittivity estimation with radar data can provide the geometric and physical parameters of near-surface materials of planetary bodies. In this study, we propose to use the adaptive genetic algorithm (AGA), which can prevent the result from converging to local minima and not subject to the initial model setting, to improve the reliability and efficiency of the estimation of the permittivity with the GPR data. Based on the convolution forward model and simulation data, the inversion method with AGA has demonstrated an excellent performance on curve fitting. The statistical probability of the estimation result proves that the AGA, compared with the standard genetic algorithm (SGA), has the ability of sustainable evolution and well convergence in the high dimension problem. In the simulation experiments, the mean value of the 95% confidence interval of the permittivity inversion results narrowed from 8.4 to 2.6. Next, we used AGA to GPR experimental data collected in the lava tube field on the Earth and successfully detected the low permittivity characteristics within the lava tubes. This result can provide a workable inversion method for finding lava tubes and other unknown subsurface features in future planetary exploration, especially lack of background information in planetary or polar exploration.