Recent applications of turning-ray tomography
Bibliographic record
Abstract
Abstract Recent applications of 2D and 3D turning-ray tomography show that near-surface velocities are important for structural imaging and reservoir characterization. For structural imaging, we used turning-ray tomography to estimate the near-surface velocities for static corrections followed by prestack time migration and the near-surface velocities for prestack depth migration. Two-dimensional acoustic finite-difference modeling illustrates that wave-equation prestack depth migration is very sensitive to the near-surface velocities. Field data demonstrate that turning-ray tomography followed by prestack time migration helps to produce superior images in complex geologic settings. When the near-surface velocity model is integrated into a background velocity model for prestack depth migration, we find that wave propagation is very sensitive to the velocities immediately below the topography. For shallow-reservoir characterization, we developed and applied azimuthal turning-ray tomography to investigate observed apparent azimuthal-traveltime variations, using a wide-azimuth land seismic survey from a heavy-oil field at Surmont, Canada. We found that the apparent azimuthal velocity variations are not necessarily related to azimuthal anisotropy, or horizontal transverse isotropy (HTI), induced by the stress field or fractures. Near-surface heterogeneity and the acquisition footprint also could result in apparent azimuthal variations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".