An Interactive Integrated Interpretation of GPR and Rayleigh Wave Data Based on the Genetic Algorithm

  • Tan Qin
  • Yonghui ZhaoEmail author
  • Shufan Hu
  • Cong An
  • Wenda Bi
  • Shuangcheng Ge
  • Lorenzo Capineri
  • Thomas Bohlen


Ground-penetrating radar (GPR) and the seismic surface wave method are two geophysical techniques commonly used in near-surface surveys up to a depth of tens of meters. GPR can be utilized to clearly distinguish lithologic interfaces, while the seismic surface wave method (hereby referred to as the Rayleigh wave) is employed for its high sensitivity to the shear (S) wave velocity. However, the propagation velocity of the electromagnetic (EM) wave which is both initiated and tracked by GPR is difficult to determine within the stratum. Unfortunately, this can affect the time–depth conversion and interpretation of the radargram. Moreover, both the horizontal resolution and the detectability of the properties of the shallow stratum are limited by the seismic geometry of the Rayleigh wave data. It is important to note that the non-uniqueness of geophysical inversion problem also generates additional biases. To overcome the problems mentioned above, we put forward an interactive integrated geophysical system that utilizes the thickness as a bridge to connect GPR and the Rayleigh wave data in the data processing and inversion. In this study, we employed the velocity of a radagram and the Rayleigh wave dispersion curve inversion based on a genetic algorithm to reconstruct the near-surface distribution. Then, we set the thickness derived from the GPR data as limit of the Rayleigh wave dispersion curve inversion. Hence, we applied the constrained inversion result to determine the most accurate EM wave velocity. As expected, both the constrained inversion of the Rayleigh wave dispersion curve and the velocity correction of the radargram performed better than did the geophysical method alone, both in the numerical models and in the field. Finally, we found the interactive integrated geophysical system to be more conducive for geological interpretation in near-surface surveys.


GPR Rayleigh wave data Integrated geophysical interpretation Genetic algorithm 



The work was supported by grants from the National Natural Science Foundation of China (Nos. 41774124 and 41374146). It was also supported by scholarships from China Scholarship Council (Nos. 201704280008 and 201806260258). The authors thank for the help from Prof. Yuzhu Liu, Mr. Xinbing Zhang, Yaohui Liu, Kai Wu and Han Song in field tests in Shanghai, China, and also thank for the help from Prof. Pingsong Zhang, Mr. Shenglin Li, Zhen Qin and Bingyang Sun in field surveys in Anhui Province, China. Also they are grateful for the assistance from Luca Bossi, Alessandro Bartolini, Pierluigi Falorni, Pietro Giannelli and other members in the group of Prof. Lorenzo Capineri working at the University of Florence, Italy. Finally, the authors thank the reviewers whose suggestions absolutely improved the paper.


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Ocean and Earth ScienceTongji UniversityShanghaiChina
  2. 2.Zhejiang University of Water Resources and Electronic PowerHangzhouChina
  3. 3.Department of Information EngineeringUniversity of FlorenceFlorenceItaly
  4. 4.Geophysics InstituteKarlsruhe Institute of TechnologyKarlsruheGermany

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