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| Home > Publications database > Evaluation of starting model approaches and effective source wavelet variations for high-frequency ground-penetrating radar full-waveform inversion |
| Journal Article | FZJ-2023-01392 |
; ; ;
2023
GeoScienceWorld
Alexandria, Va.
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Please use a persistent id in citations: doi:10.1190/geo2021-0683.1
Abstract: High-frequency ground-penetrating radar (GPR) full-waveform inversion (FWI) can enhance the characterization of small-scale structures in the subsurface below the decimeter scale. We have investigated the potential and requirements to use FWI for higher-frequency data. Thereby, we focus on the two most important criteria to achieve reliable FWI results: adequate starting models that fulfill the half-wavelength criterion and the accuracy of the effective source wavelet. Therefore, we have defined a realistic reference model, generated synthetic GPR data sets (200, 450, and 700 MHz), and tested different standard ray-based starting model methods and frequency-hopping approaches to derive results close to our reference model. Although standard starting models provide good parameter reconstruction for lower-frequency data, a frequency-hopping approach is required for the 700 MHz data. In addition, we have seen that the reconstruction of the conductivity results is more sensitive to the presence of noise (25 dB) than the permittivity tomograms. The definition of the effective source wavelets is directly linked to the accuracy of the starting models; therefore, we investigate the effect on the FWI results for high-frequency data by varying the source wavelets in terms of starting time and/or amplitude. Considering the multiparameter nature of FWI, we observe that time shifts have a greater influence on the performance of the FWI than amplitude variations. Large time shifts of approximately 0.1 ns for the 700 MHz data may lead to the failure of the inversion, whereas amplitude variations (±5% of the maximum amplitude) affect the quantitative conductivity results only (no effect on permittivity) with an increased root-mean-square error of the data of up to 20%. Using a stochastically perturbed synthetic model, we determine an improved parameter reconstruction for higher frequencies. On the basis of our findings, we develop a workflow to obtain reliable results for high-frequency GPR FWI for future users.
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