Automatic selection of reference velocities for recursive depth migration by peak search method
Bibliographic record
Abstract
Wave equation depth migration methods such as phaseshift plus interpolation, extended split-step Fourier, or Fourier finite difference plus interpolation, require a limited set of reference velocities for efficient wavefield extrapolation through laterally inhomogeneous velocity models. In basic implementations, reference velocities are selected as either a linear or a geometric progression spanning the range of model velocities. However, it is unlikely that the model velocities are distributed linearly or geometrically. If the reference velocities can be distributed statistically to more closely approximate the actual distribution, the accuracy of the extrapolation step can be improved. Key features of our automatic reference velocity selection algorithm are 1) division of the velocity distribution into clusters 2) entropy based statistical control to determine the minimal number of reference velocities required within a cluster, 3) selection of reference velocities within each cluster at or near peaks in the probability distribution, and 4) calculation of a single reference velocity if the velocity range within each cluster is less than a minimum threshold. The automatic velocity selection algorithm is implemented as part of a prestack PSPI depth migration of the 2D Marmousi model. The algorithm is suitable for 3D data, where the tradeoff between accuracy and efficiency is more pronounced.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".