An extension of the diffraction hyperbola method to layered media
Bibliographic record
Abstract
The stratigraphy of urban sub-soils is commonly quite complex and the effective use of GPR technology requires a modelling of the signal propagation as occurring into a layered structure, often made up by not flat interfaces, rather than into a homogeneous medium. Accordingly, the estimate of the signal velocity into different materials needs to be accurate, because it affects both the focusing and the positioning of the buried targets [1-2]. In this framework, we propose an extension of the diffraction hyperbola method as effective tool for retrieving the propagation velocity of the electromagnetic waves in layered media [3]. In particular, we will consider a stratified soil with two layers whose separating interface is not flat. In this case, the diffraction curves are deformed by the refraction of the waves at the buried interface and no analytic formula for the scattering is available. We demonstrate that a suitable numerical forward modelling performed with the help of the gprMax software [4] can help retrieving the value of the propagation velocity in the second layer. At the conference we will show that, if properly dealt with, the diffraction curves generated by electrically small targets can still provide information about the properties of the soil, even if the reflection at the interface makes more difficult and trickier to look into the second layer. The method can be theoretically extended to a generic number of layers, but the possibility to effectively investigate targets in the third layer (or in layers following the third one) becomes practically feasible only if the reflection at the interfaces is weak, i.e. only if the electromagnetic characteristics of the subsequent adjacent layers are quite similar to each other. Key words: Layered media, propagation velocityReferences[1] R. Pierri, G. Leone, F. Soldovieri, R. Persico, "Electromagnetic inversion for subsurface applications under the distorted Born approximation" Nuovo Cimento, vol. 24C, N. 2, pp 245-261, March-April 2001.[2] I. Catapano, L. Crocco, R. Persico, M. Pieraccini, F. Soldovieri, “Linear and Nonlinear Microwave Tomography Approaches for Subsurface Prospecting: Validation on Real Data”, IEEE Trans. on Antennas and Wireless Propagation Letters, vol. 5, pp. 49-53, 2006.[3] R. Persico G. Leucci, L. Matera, L. De Giorgi, F. Soldovieri, A. Cataldo, G. Cannazza, E. De Benedetto, Effect of the height of the observation line on the diffraction curve in GPR prospecting, Near Surface Geophysics, Vol. 13, n. 3, pp. 243-252, 2014.[4] C. Warren, A. Giannopoulos, I Giannakis, gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, Computer Physics Communications, 209, 163-170, 2016 10.1016/j.cpc.2016.08.020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".