Velocity Building by Reflection Waveform Inversion without Cycle-skipping
Bibliographic record
Abstract
Summary Reflection waveform inversion (RWI) provides estimation of low wavenumber model components using reflections generated from a migration/demigration process. The resulting model tends to be a good initial model for FWI. In fact, the optimization images to combine the migration velocity analysis (MVA) objectives (given here by RWI) and the FWI ones. However, RWI may still encounter cycle-skipping at far offsets if the velocity model is highly inaccurate. Similar to MVA, RWI is devoted to focusing reflection data to its true image positions, yet because of the cycle skipping potential we tend to initially use only near offsets. To make the inversion procedure more robust, we introduce the extended image into our RWI. Extending the model perturbations (or image) allows us to better fit the data at larger offsets even with an inaccurate velocity. Thus, we implement a nested approach to optimize the velocity and extended image simultaneously using the objective function of RWI. We slowly reduce the extension, as the image becomes focused, to allow wavepath updates from far offsets to near as a natural progression from long wavelength updates to shorter ones. Applications on synthetic data demonstrate the effectiveness of our method without much additional cost to RWI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".