Prestack depth migration of bistatic georadar data
Bibliographic record
Abstract
We find through numerical experiment and field trials (with 1m antennae spacing), however, that PSDM is actually much better than ZOM for not only the shallow part of the image but for the deeper part as well. We find that this difference is so significant that in many cases it will justify the extra cost of PSDM. In particular, we find that that the difference in image quality is due to a significant reduction in migration artifact strength and pervasiveness. We expect that, though our PSDM and ZOM algorithms are identical internally, the “stacking” process that distinguishes PSDM from ZOM gives rise to the image improvement. For our data examples, we have adapted a PSDM algorithm that is normally applied to seismic imaging for oil and gas exploration. It is a phase-shift based algorithm that accommodates lateral velocity variation, anisotropy, as well as irregular acquisition spacing. We find that this algorithm is efficient and easy to use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".