Fast 3D ground penetrating radar simulations for glaciers

Hunziker, Jürg; Slob, E.C.; Irving, James

doi:10.1016/j.cageo.2023.105320

Cited by 2 publications

(3 citation statements)

References 40 publications

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“…Results (Figure S5D) demonstrated that no significant variations are produced, even if with null conductivity a slightly higher (about 15%–20%) mean scattering amplitude is obtained. When the amount of scatterers is similar (or even higher) than the ice, that is there is a debris (frozen) layer over or in between the ice, the effect of attenuation due to conductivity increases because the bulk attenuation of the investigated volume increases (Franke et al., 2023; Hunziker et al., 2023).…”

Section: Resultsmentioning

confidence: 99%

“…However, the assumption of randomness of debris distribution allowed to take into account in our methodology also secondary scattering events and interference phenomena similar to the ones occurring also in a natural setting. As a matter of fact, as we exploited gprMax finite-difference algorithm, it takes into account the occurrence of multiple scattering in the case of overlapping debris particles, in opposition to the method recently proposed by Hunziker et al (2023), where a crucial assumption is that debris particles are far enough from each other. In gprMax simulations, when a debris particle partially overlaps another (ore even more than one) complex debris shapes are created making the model somehow more similar to a real situation, whereas the previously set rock fraction is slightly decreased.…”

Section: Assumptions and Limitationsmentioning

confidence: 99%

“…The role of GPR modelling is essential in advancing the GPR interpretation as it can provide additional information on targets by accurately reproducing the response of subsurface materials to electromagnetic (EM) waves. By simulating the propagation of these waves through different types of subsurface materials with various geometries, numerical modelling can reproduce how EM waves interact with the expected subsurface features, such as buried objects (Diamanti & Annan, 2019; González‐Huici & Giovanneschi, 2013; Kelly et al., 2021), geological structures (Giannopoulos & Diamanti, 2008; Öztürk & Drahor, 2010), water content variations (Bano, 2006), as well as a glaciers’ internal structure (Hunziker et al., 2023; Moran et al., 2003). This information can further be used to discriminate between real subsurface features and coherent noise or artefacts that can hinder interpretation.…”

Section: Introductionmentioning

confidence: 99%

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GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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Scattering is often detected when ground‐penetrating radar (GPR) surveys are performed on glaciers at different latitudes and in various environments. This event is often seen as an undesirable feature on data, but it can be exploited to quantify the debris content in mountain glaciers through a dedicated scattering inversion approach. At first, we considered the possible variables affecting the scattering mechanisms, namely the dielectric properties of the scatterers, their size, shape and quantity, as well as the wavelength of the electromagnetic (EM) incident field to define the initial conditions for the inversion. Each parameter was independently evaluated with forward modelling tests to quantify its effect in the scattering mechanism. After extensive tests, we found that the dimension and the amount of scatterers are the crucial parameters. We further performed modelling randomizing the scatterer distribution and dimension, critically evaluating the stability of the approach and the complexity of the models. After the tests on synthetic data, the inversion procedure was applied to field datasets, acquired on the Eastern Gran Zebrù glacier (Central Italian Alps). The results show that even a low percentage of debris can produce high scattering. The proposed methodology is quite robust and able to provide quantitative estimates of the debris content within mountain glaciers in different conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Assumptions and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GPR modelling and inversion to quantify the debris content within ice

Santin,

Roncoroni,

Forte

et al. 2023

Near Surface Geophysics

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Real-time dual-parameter full-waveform inversion of GPR data based on robust deep learning

Xue,

Huang,

et al. 2024

Geophysical Journal International

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Summary Ground penetrating radar (GPR) is becoming an increasingly important tool for understanding the shallow electrical structures of the earth and planets due to its adaptability to harsh detection environments, efficient data acquisition and accurate detection results. GPR full-waveform can simultaneously constrain the permittivity and resistivity of the medium, providing more comprehensive geophysical information and reducing the non-uniqueness of inversion. However, given the highly non-linear inverse problem and the massive data resulted from high temporal and spatial samplings, traditional full-waveform inversion algorithms are prohibitively costly. Inspired by Google's vision semantic segmentation system, we develop a robust deep learning-guided network that integrates geology and geophysics knowledge to support the real-time translation of zero-offset GPR data into dual-parameter electrical structures. We test our proposed network using synthetic data, which demonstrates that the algorithm can provide an accurate dual-parameter electrical model from a GPR sounding in milliseconds on a common laptop PC, exhibiting high robustness and adaptability to noise interference and extreme values of model parameters. We also apply our network to field data gathered for pollutant investigation in the US. The resulting dual-parameter structure provides a more comprehensive and realistic depiction of subsurface electrical properties and reveals the migration and aging of pollutants. Our algorithm's real-time and accurate advantages are expected to further unleash the potential of GPR technology and enable it to play a more significant role in earth and planetary exploration.

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Fast 3D ground penetrating radar simulations for glaciers

Cited by 2 publications

References 40 publications

GPR modelling and inversion to quantify the debris content within ice

GPR modelling and inversion to quantify the debris content within ice

Real-time dual-parameter full-waveform inversion of GPR data based on robust deep learning

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