Parameter crosstalk and leakage between spatially separated unknowns in viscoelastic full-waveform inversion
Bibliographic record
Abstract
ABSTRACT Elastic and attenuative effects play a major role in the determination of wave amplitudes and phases observed at seismic sensors. Viscoelastic full-waveform inversion (FWI) has the potential to recover much of the information content of measured seismic data by simultaneously accounting for these effects. However, the frequency variations and phase information present in viscoelastic FWI introduce new challenges to the inversion, especially through their impact on interparameter crosstalk. Crosstalk is typically characterized through analysis of the radiation patterns of point scatterers; however, the point scatterer model is not well suited to viscoelastic FWI because (1) attenuation introduces a significant potential for crosstalk between variables distant from one another in space and (2) interpreting the effect of frequency and phase dependence on the radiation patterns of point scatterers is not straightforward. We have introduced and examined a numerical approach for assessing the viscoelastic crosstalk modes expected for a given parameterization, optimization strategy, and acquisition geometry based on differencing various synthetic inversion results. With this approach, we have characterized the viscoelastic crosstalk for a typical parameterization for several possible acquisition geometries. Of particular note is the strong tendency for Q variables to leak into elastic variables from which they are spatially separated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".