A multiscale full-waveform inversion strategy for sparse wide-angle datasets
Bibliographic record
Abstract
The vast majority of wide-angle reflection and refraction (WARR) datasets are acquired with a relatively sparse ocean bottom seismometer (OBS) spacing. A typical WARR acquisition contains far less data to be inverted than datasets with the premeditated intention to output a model using full-waveform inversion (FWI). This work investigates the result of performing FWI on two synthetic models, a Marmousi model and an Eastern Mediterranean model. For each model, we invert both a dense dataset and a sparse dataset using a multiscale strategy. This strategy improves the result of both the fulldata and limited-data inversions, but the additional steps are designed to account for the limited-data inversions. Progressively decreasing the degree of smoothing as maximum offset increases results in a better inverted model for limited-data inversions. Successfully inverting these limited-data models proves that this multiscale inversion strategy can handle large, sparse WARR datasets. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 11:25 AM Location: Poster Station 8 Presentation Type: Poster
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".