Capineri, Lorenzo

Cong, An

Shuangcheng, Ge

Shufan, Hu

Tan, Qin

Yonghui, Zhao

English

Florence Research

Geophysical Research AbstractsVol. 20, EGU2018-7222-1, 2018EGU General Assembly 2018© Author(s) 2018. CC Attribution 4.0 license.The Integrated inversion interpretation of GPR and Rayleigh wave basedon Genetic AlgorithmTan Qin (1), Yonghui Zhao (1), Shufan Hu (1), Cong An (1), Lorenzo Capineri (2), and Shuangcheng Ge (3)(1) Tongji University, School of Ocean & Earth Sciences, Shanghai, China (zhaoyh@tongji.edu.cn), (2) University ofFlorence, Department of Information Engineering, Florence, 50139, Italy(lorenzo.capineri@unifi.it), (3) Zhejiang Universityof Water Resources and Electronic Power,Hangzhou 310018, China(gesc163@163.com)Ground Penetrating Radar (GPR) and Rayleigh Wave are general non-destructive testing (NDT) methods used inthe near surface exploration. However, loads of project practices and case studies show that the high-frequencysignals of electromagnetic wave travels in the medium with high attenuation for long travel times, and manyartifacts caused by different interference sources decrease the quality of radargram significantly. Thus the stratumof deep area becomes low S/N ratio in radar profile. Meanwhile the lateral resolution of Rayleigh wave cannotmeet the requirement of survey due to the limitation of recording geometry. What mentioned above is a bottleneckof those engineering exploration techniques. In the paper, we tackled this problem by the combination of GPR andRayleigh wave in inversion. Firstly, we achieved the numerical modelling of electromagnetic wave and surfacewave, as well as the calculation of dispersion curve connected with the fundamental surface wave mode. Thenwe extracted the stacking velocities from radar velocity spectrum produced by the cross-correlation criterion.Through Dix formula, we estimated the velocity ranges and the layer thickness in the case of horizontal layeredmedia. On the other hand, we used Genetic Algorithm (GA) to inverse the shear wave velocities, in which theprevious result of GPR was set as a constraint condition. Considering that, the solution of this stochastic inversionmethod was different every time, we adopted statistical analysis conception to the comparison the joint inversionand individual inversion respectively. The results illustrated that the method we proposed performed much betterthan either of GPR and Rayleigh wave. Specific for, radargram provided the surface wave inversion with high-resolution information about shallow strata. In return, the former also benefited from the later which possessedgreater penetrability and showed more precise layering in deep area. Finally, we conducted a series of field testsfor detecting an underground structure. The underground interfaces can be easily distinguished by the integratedmethod. It has been proved that this method combining the advantages of GPR and Rayleigh wave together will bea decent compensation for the existing geophysical methods.

The Integrated inversion interpretation of GPR and Rayleigh wave based on Genetic Algorithm

https://flore.unifi.it/bitstream/2158/1126823/1/EGU2018-7222-1.pdf

The Integrated inversion interpretation of GPR and Rayleigh wave based on Genetic Algorithm

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