Towards 3D full-waveform inversion of crosshole GPR data

Mozaffari, Amirpasha; Klotzsche, Anja; He, Guowei; Vereecken, Harry; Kruk, Jan van der; Warren, Craig; Giannopoulos, Antonios

doi:10.1109/icgpr.2016.7572687

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…As shown in Mozaffari et al (2016), the results of the 2D FWI with a limited number of iterations can be used to improve the starting models for the 2.5D FWI, which allows a faster convergence and hence reduces the computational effort. Therefore, we applied 2D FWI to create ε r starting models at iterations 1, 4 and 7, and then we used them for the 2.5D FWI.…”

Section: Cascade 25d Fwimentioning

confidence: 99%

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2.5D crosshole GPR full-waveform inversion with synthetic and measured data

et al. 2020

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Full-waveform inversion (FWI) of cross-borehole ground-penetrating radar (GPR) data is a technique with the potential to investigate subsurface structures. Typical FWI applications transform 3D measurements into a 2D domain via an asymptotic 3D to 2D data transformation, widely known as a Bleistein filter. Despite the broad use of such a transformation, it requires some assumptions that make it prone to errors. Although the existence of the errors is known, previous studies have failed to quantify the inaccuracies introduced on permittivity and electrical conductivity estimation. Based on a comparison of 3D and 2D modeling, errors could reach up to 30% of the original amplitudes in layered structures with high-contrast zones. These inaccuracies can significantly affect the performance of crosshole GPR FWI in estimating permittivity and especially electrical conductivity. We have addressed these potential inaccuracies by introducing a novel 2.5D crosshole GPR FWI that uses a 3D finite-difference time-domain forward solver (gprMax3D). This allows us to model GPR data in 3D, whereas carrying out FWI in the 2D plane. Synthetic results showed that 2.5D crosshole GPR FWI outperformed 2D FWI by achieving higher resolution and lower average errors for permittivity and conductivity models. The average model errors in the whole domain were reduced by approximately 2% for permittivity and conductivity, whereas zone-specific errors in high-contrast layers were reduced by approximately 20%. We verified our approach using crosshole 2.5D FWI measured data, and the results showed good agreement with previous 2D FWI results and geologic studies. Moreover, we analyzed various approaches and found an adequate trade-off between computational complexity and accuracy of the results, i.e., reducing the computational effort while maintaining the superior performance of our 2.5D FWI scheme.

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Section: Cascade 25d Fwimentioning

confidence: 99%

“…We propose a second strategy, where we combine the methods of Klotzsche et al (2012) and Mozaffari et al (2016). Thereby, we update only the ε r starting model with essential features revealed in the 2D FWI.…”

Section: 5d Fwi With Updated ε R Starting Modelmentioning

confidence: 99%

2.5D crosshole GPR full-waveform inversion with synthetic and measured data

et al. 2020

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“…Currently, the most ubiquitous approach towards permafrost monitoring is borehole temperature time-lapse surveys [35][36][37]. Next in abundance are geoelectric prospecting techniques, including their cross-borehole modifications and 3D data inversion: electrical resistivity tomography [38][39][40][41] and ground penetrating radar [42][43][44]. There exist experimental works that simulate resistivity monitoring of the active permafrost layer in laboratory conditions [45].…”

Section: Introductionmentioning

confidence: 99%

Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

Glinskikh,

Nechaev,

Mikhaylov

et al. 2024

Mathematics

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Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. We propose a cross-borehole exploration system for a high localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We consider numerical simulation results of the transient electromagnetic monitoring of industrial facilities located on permafrost. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.

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