A tunneling approach for clustered priors in full-waveform inversion
Bibliographic record
Abstract
While seismic data is an extremely useful source of information, many seismic inversion problems can be difficult or impossible without the use of supplementary prior information. Knowledge of plausible rock-types in a given setting, available from well logs and/or geological understanding, can, in particular, be an effective supplement to seismic data. However, when this prior information describes clustering in rock physics or elastic-parameter properties, it can be difficult to effectively include in full waveform inversion due to the limitations of the local optimization strategies used. In particular, regularization terms based on clustering-type information can create local minima, which are prone to hindering convergence with conventional FWI optimization approaches. Here, we propose an optimization strategy for full waveform inversion, in which global regularization information is partially accounted for, and in which a model can, under the right circumstances, “tunnel” between basins, to honour prior information. This tunneling is implemented in conjunction with conventional, local optimization strategies; after each local model update, a second update follows, in which each grid cell tunnels to a new cluster if the move is warranted by the update history of the model (its “momentum”) and the model regularization penalty (its “potential”). With a synthetic example, we illustrate that this approach offers the potential to improve upon conventional strategies when prior information represents rock-type clustering.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".