Parameter crosstalk and modeling errors in viscoacoustic seismic full-waveform inversion
Bibliographic record
Abstract
ABSTRACT Simultaneous use of data within relatively broad frequency bands is essential to discriminating between velocity and Q errors in the construction of viscoacoustic full-waveform inversion (QFWI) updates. Individual frequencies or narrow bands in isolation cannot provide sufficient information to resolve parameter crosstalk issues in a surface seismic acquisition geometry. At the same time, too broad a frequency band introduces significant problems in the presence of modeling errors. The risk of modeling errors arising in QFWI is high because of the range of very different geologic contributors to attenuation and dispersion and the variety of available mathematical descriptions. We perform numerical tests that suggest that by relaxing the typical requirement that the frequency dependence of the assumed intrinsic attenuation model be self-consistent across the full spectrum, significant improvement in the fidelity of the inversion results can be obtained in cases where the attenuation model assumed in the inversion differs substantially from the true subsurface behavior. We find that the size of the frequency bands used in this inversion approach is a useful hyperparameter controlling trade-off between crosstalk reduction and flexibility in coping with uncertain physics.
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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".