Application of an unbalanced optimal transport distance and a mixed L1/Wasserstein distance to full waveform inversion
Bibliographic record
Abstract
SUMMARY Full waveform inversion (FWI) is an important and popular technique in subsurface Earth property estimation. In this paper, several improvements to the FWI methodology are developed and demonstrated with numerical examples, including a simple two-layer seismic velocity model, a cross borehole Camembert model and a surface seismic Marmousi model. We introduce an unbalanced optimal transport (UOT) distance with Kullback–Leibler divergence to replace the L2 distance in the FWI problem. Also, a mixed L1/Wasserstein distance is constructed that preserves the convex properties with respect to shift, dilation, and amplitude change operation. An entropy regularization approach and convolutional scaling algorithms are used to compute the distance and the gradient efficiently. Two strategies of normalization methods that transform the seismic signals into non-negative functions are discussed. The numerical examples are then presented at the end of the paper.
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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".