Central-Frequency Misfit Function for Full-Waveform Q Inversion in 2D/3D Viscoelastic Medium
Bibliographic record
Abstract
Summary High-resolution Q models can provide important information for identifying subsurface hydrocarbon reservoirs. Full-waveform inversion (FWI) can generate accurate Q models by numerically solving the viscoelastic wave equation. However, the velocity-Q trade-offs significantly complicate Q waveform inversion. In this study, a new central-frequency misfit function is designed to reduce the trade-off artifacts for Q waveform inversion. Compared to traditional waveform-difference misfit function, this new approach is less sensitive to velocity variations and thus can invert for the Q models more stably. Synthetic examples in 2D/3D viscoelastic medium are given to verify the advantages of centralfrequency misfit function for Ｑ waveform inversion.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".