Amplitude-based misfit functions in viscoelastic full-waveform inversion applied to walk-away vertical seismic profile data
Bibliographic record
Abstract
ABSTRACT Viscoelastic full-waveform inversion is applied to walk-away vertical seismic profile data acquired at a producing heavy-oil field in Western Canada for the determination of subsurface velocity models (P-wave velocity α and S-wave velocity β) and attenuation models (P-wave quality factor Qα and S-wave quality factor Qβ). To mitigate strong velocity-attenuation trade-offs, a two-stage approach is adopted. In Stage I, α and β models are first inverted using a standard waveform-difference (WD) misfit function. Following this, in Stage II, different amplitude-based misfit functions are used to estimate the Qα and Qβ models. Compared to the traditional WD misfit function, the amplitude-based misfit functions exhibit stronger sensitivity to attenuation anomalies and appear to be able to invert Qα and Qβ models more reliably in the presence of velocity errors. Overall, the root-mean-square amplitude-ratio and spectral amplitude-ratio misfit functions outperform other misfit function choices. In the final outputs of our inversion, significant drops in the α to β ratio (~1.6) and Poisson’s ratio (~0.23) are apparent within the Clearwater Formation (depth ~0.45–0.50 km) of the Mannville Group in the Western Canada Sedimentary Basin. Strong Qα (~20) and Qβ (~15) anomalies are also evident in this zone. These observations provide information to help identify the target attenuative reservoir saturated with heavy-oil resources.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".