Combined migrations and time-depth conversions: first results
Bibliographic record
Abstract
Commonly exploited migration algorithms or also well-established linear inverse scattering algorithms [1] for the focusing of GPR data are often based on the hypothesis of a homogeneous soil. However, this assumption is not valid always, and it provides deformed results when it is applied to image scenarios where it is not valid. More complex models of the scattering can afford the situation of a stratified medium, but only if the layers are flat and parallel to each other these model assumes analytic forms. In any case, commonly available commercial codes do not allow to implement the reflections and refraction of the waves associated to these cases [2]. More recently, time reverse migration algorithms have been introduced. They can account efficiently of non-homogeneous soils, but their performances in case of large and strong scattering targets are not yet completely established and they make use of forward numerical solvers, not all the times available and user friendly. At the conference, we will describe a strategy based on suitable combination of migration results achieved from different homogeneous media, accompanied by a time-depth conversion accounting for the occurrence of different values of the wave propagation velocity in the investigated domain. We will show how an improvement of the imaging result is achieved even in the lack of a correct mathematical model of the scattering phenomenon. Last but not least, the proposed strategy exploits software routines easy to be implemented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".