A Comparison of Prestack Depth and Prestack Time Imaging of the Paktoa Complex, Canadian Beaufort MacKenzie Basin
Bibliographic record
Abstract
Abstract The Canadian Beaufort MacKenzie Basin (BMB) contains many examples on two-dimensional seismic sections of what appear to be shale diapirs or shale-cored anticlines. Devon Canada acquired three-dimensional (3-D) seismic data across one such feature called Paktoa and, in the winter of 2006, drilled an exploration well flanking the possible shale diapir. Although the well successfully tested oil from the Eocene Taglu unit, no clear evidence existed that the structure was a shale diapir instead of a shale-cored or fractured anticline. Tests of a Kirchoff prestack depth migration (PSDM) algorithm prior to drilling had resulted in no significant improvement. Following the drilling of the Paktoa C-60 well, Applied Geophysical Services used their proprietary beam algorithm to image very steeply dipping reflectors in the core of the Paktoa structure. Interpretation of this new seismic volume shows that Paktoa is an inversion anticline, likely formed by the reactivation of an earlier extensional fault system, and not a shale diapir. The beam PSDM took just three weeks to apply to 400 km 2(154 mi 2) of marine 3-D seismic data, and the vastly improved imaging has not only changed the interpretation of the Paktoa structure but also implies that many similar features in the Canadian BMB may also be steeply dipping anticlines instead of shale diapirs or shale-cored anticlines.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".