Deconvolution imaging conditions and cross-talk suppression
Bibliographic record
Abstract
Abstract Deconvolution imaging conditions offer improved resolution over standard, crosscorrelation-based imaging conditions. Additionally, these imaging conditions produce a result more directly related to a reflection coefficient than do crosscorrelation-based imaging conditions. In simple analytical cases, deconvolution imaging conditions also offer the possibility of eliminating crosstalk (i.e., energy in the image due to reflected energy arriving at a location at the same time as incident energy that did not cause the reflected energy) when the full up- and down-going wavefields are used. This means that in such cases, surface-related multiples can be eliminated from the image, or that multiple shots could potentially be fired simultaneously without degrading the image. However, this cross-talk-suppression property is not observed in most situations. We show that this is due to a number of issues: the correct order of deconvolution must be used, stabilization causes imperfect deconvolution, finite apertures lead to some of the signal being lost, and an assumption of horizontal stratification is often not being met. Further, imperfect knowledge of the incident and reflected field due to such factors as anisotropy, poorly estimated velocity fields, and measurement noise can also lead to imperfect deconvolution. Thus, deconvolution imaging conditions should not be counted on to completely eliminate crosstalk from images.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".