Parameter cross-talk and leakage between spatially-separated unknowns in viscoelastic FWI
Bibliographic record
Abstract
Elastic and attenuative effects play a major role in the determination of seismic wave amplitudes. Viscoelastic FWI has the potential to recover more information from measured data by accounting for these effects. A major obstacle to the effective use of viscoelastic FWI is inter-parameter cross-talk. This is typically characterized through the use of radiation patterns, but these are not well suited to viscoelastic FWI, because (1) there is significant potential for cross-talk between variables distant from one another in space, and (2) interpreting the effect of frequency and phase dependence in radiation patterns is not straightforward. We present and examine a numerical approach to assessing viscoelastic cross-talk. With it, we observe strong cross-talk both between velocity and Q variables, and into density for a variety of acquisition geometries. Of particular note is our characterization of the tendency for Q variables to leak into elastic variables from which they are spatially separated. This type of cross-talk is not easily characterized through the use of radiation patterns. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 10:35 AM Location: 302B Presentation Type: Oral
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".