A regularization by denoising (RED) scheme for 3-D FWI model updates in large-contrast media
Bibliographic record
Abstract
SUMMARY Full waveform inversion (FWI) endeavours to estimate high-resolution physical properties of subsurface structures. The technique minimizes the data misfit between observed and modelled seismograms. Despite its success, the application of FWI in areas with high-velocity contrasts remains a challenging problem. Often, quadratic regularization methods are chosen to stabilize inverse problems. Unfortunately, quadratic regularization does not preserve edges and sharp discontinuities adequately. Conversely, a regularization term that uses the l1 norm of the gradient of model parameters can preserve discontinuities. The latter leads to edge-preserving methods based on total variation regularization. This work adopts the framework named regularization by denoising (RED) to solve the FWI problem in high-contrast media. The RED technique only requires an image denoising engine, which, in our case, is a modified weighted total variation filter. One advantage of adopting the RED algorithm for solving FWI problems is its simplicity in the numerical implementation and selection of trade-off parameters. We have benchmarked our algorithm via the 2-D BP/EAGE model, a model with significant velocity contrasts and complex salt bodies. We have also tested the proposed regularization method with the 3-D SEG/EAGE overthrust P-wave velocity model. We also compare the proposed RED method and FWI with total variation regularization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".