Attenuation of swell noise in marine streamer data via nonnegative matrix factorization
Bibliographic record
Abstract
High-amplitude swell noise is a common sort of noise in marine seismic surveys. It can obscure the lower amplitude signals of interest and result in erroneous imaging and consequently wrong interpretations of the data. Therefore, it is of great importance to attenuate them in the early processing steps. In this paper we propose a new alternating minimization technique for swell noise attenuation which is based on non-negative matrix factorization of the power spectrum of the time-frequency representation of the signal and noise components of the data. The key element of our method is that the noise has sufficiently different time-frequency characteristics with respect to the signals of interest. The noise component is trained by a data-driven dictionary learning step. We have successfully applied this technique to a shot gather largely contaminated by swell noise. The swell noise is reduced significantly with very little impacts on the useful signals. We suggest that this method can be applied to ground roll attenuation as well. Presentation Date: Monday, October 17, 2016 Start Time: 3:45:00 PM Location: 140 Presentation Type: ORAL
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".