Vector-acoustic full-waveform inversion: Taking advantage of wavefield separation and dealiasing
Bibliographic record
Abstract
ABSTRACT Vector-acoustic full-waveform inversion (VAFWI) directly inverts vector-acoustic (VA) data, which consist of pressure and particle displacement components, at the cost of conventional acoustic full-waveform inversion (FWI). VA data contain information about the direction of arrival of the recorded seismic waves. In VAFWI, this directional information is taken into account by introducing an appropriate data weighting. With this weighting, in the geometry of a marine seismic experiment, the VAFWI adjoint calculation approximates inverse wavefield extrapolation, resulting in the natural separation of up- and downgoing recorded waves. If the free-surface effects are modeled during the inversion, the wave separation leads to (1) suppression of surface-related artifacts, (2) constructive interference of receiver ghosts with their primaries leading to preservation of the low-frequency content in the adjoint fields, and (3) compensation for insufficient spatial wavefield sampling on the receiver side. The horizontal displacement component helps interpolate the missing data. Synthetic examples demonstrate that for undersampled data, VAFWI consistently recovers the subsurface properties with higher resolution and fewer artifacts than conventional FWI.
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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".