Interpretive velocity‐model building for seismic data acquired across a complex structure in Southern Alberta, Canada
Bibliographic record
Abstract
We processed in time and depth two seismic lines from an area of extremely complex geology in the Turtle Mountain area of Southern Alberta, Canada. The time processing was designed to attenuate noise and enhance signal in the data. To develop a velocity model for pre-stack depth migration (PSDM) we integrated all available sources of velocity and geological information. We used the subweathering velocity models derived from refraction statics analysis together with constant velocity migrations for velocity information in the shallow section. The locations of velocity pull-ups on the time migrated sections acted as guides to the extent of high-velocity carbonates carried in the hangingwall of a major thrust fault. We integrated the mapped surface geology, geological cross-sections, well depths and interval velocities from sonic logs into the velocity model. The depth processed sections show a more realistic geometry than the time sections for the reflectors at depth. The seismic data cannot image the shallow, steeply dipping strata in Turtle Mountain itself. Our interpretations of the depth sections are based upon published geological models, surface geology maps, well data and the seismic character where the reflections are imaged adequately.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".