Velocity Building by Reflection Waveform Inversion without Cycle-skipping
Why this work is in the frame
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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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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 | 0.001 | 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) | 0.000 | 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 it