Bandwidth enhancement: Inverse Q filtering or time-varying Wiener deconvolution?
Bibliographic record
Abstract
ABSTRACT Dispersion and attenuation corrections can improve the resolution of seismic data. This significantly facilitates interpretation. In principle, inverse Q filtering and the time-varying Wiener deconvolution can achieve this. Inverse Q filtering is a deterministic process that requires knowledge of the quality factor Q, whereas the time-varying Wiener deconvolution is a statistical approach based on the estimation of the nonstationary propagating wavelet. Dispersion corrections based on phase-only inverse Q filtering is an inherently stable method that is robust in the presence of noise. Attenuation corrections via amplitude-only inverse Q filtering, on the other hand, is likely to lead to noise amplification as well as bandwidth enhancement. Dispersion corrections via the time-varying Wiener deconvolution are challenging because these require estimation of a nonstationary, frequency-dependent, nonminimum-phase wavelet. Fortunately, attenuation corrections via the Wiener deconvolution need only estimation of a zero-phase time-varying wavelet for which robust methods exist. The most promising procedure for combined dispersion and attenuation correction is thus comprised of first applying dispersion corrections using phase-only inverse Q filtering, followed by zero-phase time-varying Wiener deconvolution.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".