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 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) | 0.001 | 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 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".