NuSPAN: A Proximal Average Network for Nonuniform Sparse Model --\n Application to Seismic Reflectivity Inversion
Bibliographic record
Abstract
We solve the problem of sparse signal deconvolution in the context of seismic\nreflectivity inversion, which pertains to high-resolution recovery of the\nsubsurface reflection coefficients. Our formulation employs a nonuniform,\nnon-convex synthesis sparse model comprising a combination of convex and\nnon-convex regularizers, which results in accurate approximations of the l0\npseudo-norm. The resulting iterative algorithm requires the proximal average\nstrategy. When unfolded, the iterations give rise to a learnable proximal\naverage network architecture that can be optimized in a data-driven fashion. We\ndemonstrate the efficacy of the proposed approach through numerical experiments\non synthetic 1-D seismic traces and 2-D wedge models in comparison with the\nbenchmark techniques. We also present validations considering the simulated\nMarmousi2 model as well as real 3-D seismic volume data acquired from the\nPenobscot 3D survey off the coast of Nova Scotia, Canada.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".