Noise-Tolerant Sparse Spike Deconvolution based on Convolutional Phase Retrieval
Bibliographic record
Abstract
Summary We have developed a noise-tolerant sparse spike deconvolution algorithm. The sparsity-based deconvolution algorithms are sensitive to noise, and they struggle to cope with noisy data. The seismic noise can drastically degrade the phase; however, the amplitude spectrum, especially in the band-width defined by the source signature, is more resilient to noise. Hence, we adopt the convolutional phase retrieval concept as a constraint to develop a noise-tolerant deconvolution algorithm. We designed a non-linear cost function based on data fitting in time and frequency domain along with the sparsity constraint on the reflectivity series. This non-linear optimization is solved with the L-BFGS method. As the proposed method is a local minimization algorithm, we carefully initialize the algorithm based on the spectral method. Our results show that the algorithm can tolerate noise content as low as SNR=2.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".