Combined Migration of GPR data for layered media
Bibliographic record
Abstract
In this contribution we will propose the combination of migration results achieved from the same GPR dataset, aimed to mitigate the effects of the nonuniformity of the propagation velocity of the waves throughout the investigated domain. The nonuniformity of the propagation velocity can be appreciated from the diffraction hyperbolas [1] possibly present in the data, or directly from the results of the focusing [2] achieved from different trial values of the propagation velocity. In ref. [3] an algebraic combination of two (but theoretically even more) migration results achieved from different migration parameters applied to the same data has been shown. In that paper, the case of a horizontal variation and the case of a vertical variation of the propagation velocity of the electromagnetic waves in the soil were considered. Here, we will consider the case of a layered medium with non-flat interface between two adjacent layers, which is a case of interest in several practical application, and is a case where we have both a vertical and a horizontal variation of the parameters. Analogously to ref. [3], we will consider both the aspect of the focusing and that of the combined time-depth conversion. References [1] 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, 2015. [2]G. Gennarelli, I. Catapano, F. Soldovieri, R. Persico, On the Achievable Imaging Performance in Full 3-D Linear Inverse Scattering, IEEE Trans. on Antennas and Propagation, vol. 63, n. 3, pp. 1150-1155, March 2015. [3] R. Persico, G. Morelli, Combined Migrations and Time-Depth Conversions in GPR Prospecting: Application to Reinforced Concrete, Remote Sens. 2020, Volume 12, Issue 17, 2778, open access, DOI 10.3390/rs12172778
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".