High-amplitude noise detection by the expectation-maximization algorithm with application to swell-noise attenuation
Bibliographic record
Abstract
Abstract High-amplitude noise is a common problem in seismic data. Current filtering techniques that target this problem first detect the location of the noise and then remove it by damping or interpolation. Detection is done conventionally by comparing individual data amplitudes in a certain domain to a user-controlled local threshold. In practice, the threshold is optimally tuned by trial and error and is often changed to match the varying noise power across the data set. We have developed an automatic method to compute the appropriate threshold for high-amplitude noise detection and attenuation. The main idea is to exploit differences in statistical properties between noise and signal amplitudes to construct a detection criterion. A model that consists of a mixtureof two statistical distributions, representing the signal and the noise, is fitted to the data. Then it is used to estimate the probability (i.e., likelihood) that each sample in the data is noisy by means of an expectation-maximization (EM) algorithm. Only those samples with a likelihood greater than a specific threshold are considered to be noise. The resulting probability threshold is better adapted to the data compared to a conventional amplitude threshold. It offers the user, through the probability threshold value, the possibility to quantify the confidence in whether a large amplitude anomaly is considered as noise. The method is generic; however, our work develops and implements the method for swell-noise attenuation. Initial results are encouraging, showing slightly better performance than an optimized conventional method but with much less parameter testing and variation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".