L<sub>p</sub>-space robust matching pursuit algorithm for time-domain Radon-based deblending
Bibliographic record
Abstract
Sparse solutions of linear systems of equations are important in many applications of seismic data processing. These systems occur in many denoising algorithms, such as those that use Radon transforms. We propose a robust Matching Pursuit algorithm for the retrieval of sparse Radon domain coefficients. The algorithm is robust to outliers and, hence, applicable to simultaneous source separation. The classical Matching Pursuit algorithm is frequently adopted to approximate data by a small number of basis functions. It performs efficiently for data contaminated with Gaussian random noise. On the other hand, Matching Pursuit tends to identify the incorrect basis functions when erratic noise contaminates seismic data such as in the case of source interferences in common receiver gathers of simultaneous source surveys. The inclusion of a lp space inner product into the Matching Pursuit algorithm enables to enhance its robustness to erratic signals significantly. We also provide a detailed description of steps required to implement the proposed lp space Robust Matching Pursuit algorithm when the basis functions are not in an explicit form, such as it occurs with the time-domain Radon transform. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 11:00 AM Location: Poster Station 13 Presentation Type: Poster
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".