Observed azimuthai anisotropy could be caused by near-surface heterogeneity
Bibliographic record
Abstract
Near-surface velocities could vary with azimuth, impacting seismic data processing and interpretation. In this study, we developed a methodology to investigate the variations of near-surface velocities with azimuth, using 3D turning-ray tomography. The input data are the first arrivals selected from pre-defined azimuth sectors in terms of shot-receiver-pair directions. The output velocities from tomography correspond to the selected azimuth sectors. A near-surface tomography study based on seismic data from a shallow heavy-oil reservoir in Canada has suggested that the observed azimuthal traveltime variations are not necessarily related to azimuthal (HTI) anisotropy induced by the stress field or fractures. It could also be caused by the near-surface heterogeneity or acquisition footprint. Near-surface complexity could masquerade as anisotropy. Potentially this can influence statics and prestack imaging.
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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 | 0.001 |
| Open science | 0.001 | 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".