An efficient variable-splitting multiplier method for compressive sensing seismic data reconstruction
Bibliographic record
Abstract
We use compressive sensing theory for seismic data reconstruction. Compressive sensing, in part, requires an optimization model. We consider two classes of optimization models that have been presented in the compressive sensing literature: synthesis- and analysis-based optimization models. For the analysis-based optimization model, we introduce a novel optimization algorithm, SeisADM. SeisADM adapts the alternating direction method with a variable-splitting technique, taking advantage of the structure intrinsic to the seismic data reconstruction problem to help give an efficient and robust algorithm. We use SeisADM to solve a seismic data reconstruction problem for both synthetic and real data examples. In both cases, we compare the SeisADM results to those obtained from using a synthesis-based optimization model. We use the SPGL1 method to compute the synthesis-based results. We observe, through both examples, that data reconstruction results based on the analysis-based optimization model are more accurate than the results based on the synthesis-based optimization model. In addition, we observe that for seismic data reconstruction, the SeisADM method requires less computation time than the SPGL1 method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".