Eliminating harmonic noise in vibroseis data through sparsity-promoted waveform modeling
Bibliographic record
Abstract
ABSTRACT Vibroseis acquisition, which uses slip sweep instead of traditional flip-flop acquisition, could significantly reduce cycle time and increase productivity. However, the vibroseis system suffers from harmonically distorted sweeps being used as correlation operators, thus causing sticky harmonic distortions in correlated data that cannot be eliminated by forerunning manipulations and hindering interpretation. We propose a novel method to separate the harmonic interferences from correlated vibroseis data by exploring the waveform diversity between useful reflections and harmonic interferences. Following the diverse time-frequency distribution patterns of useful signal components and harmonic interferences, two different redundant waveform dictionaries are constructed to sparsely model useful reflections and harmonic interferences. Then, an iterative thresholding algorithm is used to gradually separate harmonic interferences from useful reflections, with each successive iteration potentially extracting the most reliable waveform elements built up into the corresponding signal components. The processing results of synthetic and field data examples highlight the effectiveness of our method in eliminating harmonic noise without noticeable loss of useful reflections. Compared to the classic frequency-dependent attenuation method, our approach has a higher fidelity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".