Constraining Acoustic Impedance Inversion by Seismic-processing Velocities
Bibliographic record
Abstract
Summary Reflection seismic data are often transformed into acoustic-impedance (AI) pseudo-logs for quantitative reservoir prediction. However, a well-known difficulty of AI inversion methods consists in the lack of low-frequency information in seismic records. This missing information can be partly recovered from stacking, interval, and migration velocities derived from seismic processing. Here, seismic-processing (stacking) velocities are transformed into the low-frequency impedances and calibrated by the impedances calculated from acoustic logs. Empirical non-linear relations between the seismic-processing and well-log impedances are derived. These dependences are further extrapolated to 3-D data volumes and used as low-frequency constraints on AI inversion. The approach is incorporated in a high-quality AI inversion method and illustrated on a time-lapse 3-D 3-C dataset from Weyburn CO2 sequestration project in southern Saskatchewan.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".