Aliasing in wavefield extrapolation prestack migration
Bibliographic record
Abstract
Abstract Aliasing during migration (“operator aliasing”) is widely recognized as a problem for Kirchhoff migration. It occurs when high-frequency reflection data are swept out at steep angles, with the problem being worst for very coarse input-trace spacing. The problem can be solved either by data interpolation to a finer-spaced grid of input traces or, more commonly, by anti-aliasing the migration operator (Gray, 1992; Lumley et al., 1994; Abma et al., 1999; Biondi, 2001, Zhang et al., 2001a). Although there is no general agreement on the best way to perform Kirchhoff migration anti-aliasing for all problems, especially those involving irregular spatial sampling, there is agreement on the underlying principle. That is, the diffraction surface used by the migration to accumulate data into the image at a single point should sample the input traces adequately (Figure 1). For two-dimensional (2D) poststack migration, “adequately” means that the diffraction curve must sample adjacent traces with a time delay no greater than one-half period at any frequency present in the data.
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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 | 0.000 |
| 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".