Spectral decomposition with <i>f</i>−<i>x</i>−<i>y</i> preconditioning
Bibliographic record
Abstract
ABSTRACT Spectral decomposition, or local time‐frequency analysis, tries to enhance the amount of information one can obtain from a seismic volume by finding the frequency content of the seismic data at each time sample. However, if a small amount of noise is present within the seismic amplitude volume, it has the potential to become more prominent in the spectrally decomposed data especially if high‐resolution or sparsity promoting methods are utilized. To combat this problem post‐processing noise removal has commonly been employed, but these techniques can potentially degrade the resolution of small‐scale geological structures in their attempt to remove this noise. Rather than de‐noising the spectrally decomposed data after they are generated, we propose to incorporate the ideas of f−x−y deconvolution within the spectral decomposition process to create an algorithm that has the ability to de‐noise the time‐frequency representation of the data as they are being generated. By incorporating the spatial prediction error filters that are utilized for f−x−y deconvolution with the spectral decomposition problem, a spatially smooth time‐frequency representation that maintains its sparsity, or high‐resolution characteristics, can be obtained. This spatially smooth high‐resolution time‐frequency representation is less likely to exhibit the random noise that was present in the more conventionally obtained time‐frequency representation. Tests on a real data set demonstrate that by de‐noising while the time‐frequency representation is being constructed, small‐scale geological structures are more likely to maintain their resolution since the de‐noised time‐frequency representation is specifically built to reconstruct the data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".