Adaptive linear prediction filtering for random noise attenuation
Bibliographic record
Abstract
We propose an algorithm to compute time and space variant prediction filters for signal-to-noise ratio enhancement. Prediction filtering for seismic signal enhancement is, in general, implemented via filters that are estimated from the inversion of a system of equations in the t-x or f-x domain. In addition, prediction error filters are applied in small windows where the data can be modeled via a finite number of plane waves. Our algorithm, on the other hand, does not require the inversion of matrices. Furthermore, it does not require spatio-temporal windowing; the algorithm is implemented via a recursive scheme where the filter is continuously adapted to predict the signal. We postulate the prediction problem as a local smoothing problem and use a quadratic constraint to avoid solutions that model the noise. The algorithm uses a t-x recursive implementation where the prediction filter for a given observation point is estimated via a simple rule. It turns out that the proposed algorithm is equivalent to the LMS (Least Mean Squares) filter often used for adaptive filtering. It is important to mention, however, that our derivation follows the framework that it is often used to solve underdetermined linear inverse problems. The latter involves the minimization of a cost function that includes a quadratic constraint to guarantee a stable solution. Synthetic and real data examples are used to test the algorithm. In particular, a field data test shows that adaptive t-x filtering could offer an efficient and versatile alternative to classical f-x deconvolution filtering.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".