Application of turning-ray tomography to Hussar 2D seismic line from central Alberta
Bibliographic record
Abstract
Summary Turning-ray tomography is a good tool for estimating near surface velocity structure, especially in areas where conventional refraction statics fail such as the case of a hidden layer. The velocity model from turning-ray tomography can be used for static correction, as a starting model for Full Waveform Inversion (FWI), and for wave equation datuming or prestack depth migration. In the work presented here, we apply turning-ray tomography to the statics problem of the Hussar 2D seismic line from central Alberta. In literature, the application of turning-ray tomography to the statics problem is commonly referred to as tomostatics. The traveltime tomography approach used in this study is similar to the constrained, damped, simultaneous, iterative reconstruction technique (CDSIRT) of Zhu et al, (1992). To verify results from tomostatics, we compare datasets after tomostatics with datasets using the delay-time method of conventional refraction statics. Our result shows that the velocity model from turning-ray tomography reveals a hidden, low-velocity layer (LVL) between two fast-velocity layers that conventional refraction statics would not detect. The hidden layer is in agreement with the interval velocities from well logs. The stacked section, after applying tomostatics, shows better continuity of events compared to the stacked section from conventional refraction statics.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".