Bibliographic record
Abstract
Abstract Gaussian beam migration is a depth migration method whose accuracy rivals that of migration by wavefield extrapolation — so-called “wave-equation migration” — and whose efficiency rivals that of Kirchhoff migration. This migration method can image complicated geologic structures, including very steep dips, in areas where the seismic velocity varies rapidly. However, applications of prestack Gaussian beam migration either have been limited to common-offset common-azimuth data volumes, and thus are inflexible, or suffer from multiarrival inaccuracies in a common-shot implementation. In order to optimize both the flexibility and accuracy of Gaussian beam migration, I present a common-shot implementation that handles multipathing in a natural way. This allows the migration of data sets that can include a variety of azimuths, and it allows a simplified treatment of near-surface issues. Application of this method to model data typical of Canadian Foothills structures and to model data that includes a complicated salt body demonstrates the accuracy and versatility of the migration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".