Robust reconstruction via orthogonal matching pursuit with Fourier operators
Bibliographic record
Abstract
Interpolation and denoising are critical steps in processing seismic data. The Matching Pursuit algorithm is frequently used in seismic data interpolation and noise attenuation. We propose a robust Orthogonal Matching Pursuit Fourier inter- polation (R-OMPFI) algorithm that operates the frequency- wavenumber (f − k) domain to interpolate seismic data and attenuate erratic noise simultaneously. The proposed algorithm uses an f − k angular search algorithm to identify dominant dips. Then, then Fourier coefficients located within preassigned dip corridors are selected and fit to the data via a robust solver. In other words, we adopt a cost function that directly utilizes the selected coefficients to fit synthesized signals via robust M-estimators in the time-space domain. We show that the proposed algorithm is resistant to erratic noise, making it attractive to applications such as simultaneous source deblending and reconstruction of noisy onshore datasets. The proposed methods can interpolate seismic data with different sampling schemes such as regularly decimated data and randomly sampled data on a regular grid. Both synthetic and real seismic data are being tested to examine the performance of the proposed algorithm.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".