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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".