Data-driven tomographic velocity analysis in tilted transversely isotropic media: A 3D case history from the Canadian Foothills
Bibliographic record
Abstract
Abstract We evaluated how velocity and anisotropy model-building strategies affect seismic imaging in the Canadian Foothills Thrust Belt by comparing the results of a model-driven approach with those of a data-driven approach. Two independently run Kirchhoff prestack depth-imaging projects were initiated using different static corrections for near-surface weathering layers and using different velocity and anisotropy model-building strategies. We observed that an isotropic data-driven reflection tomography velocity model-building approach resulted in a significantly better stack image than did a highly interpretive anisotropic model-driven velocity model-building approach. By carefully introducing anisotropy into the former, data-driven approach, we achieved significant improvements in positioning, including more accurate depth ties between the seismic image and well tops and better definition of structural geometries. The differences in the imaging observed at the various stages of this case history illustrate the sensitivity of the final depth images to the treatment of the near-surface velocity field, the macrointerval velocity model-building technique, and the choices of ε and δ, which are the Thomsen anisotropy parameters for tilted transverse isotropy. The data-driven approach successfully challenged the historical idea that we must perform a geologic interpretation of the seismic data to derive an accurate depth velocity model in a complex geologic setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".