Coherent Noise Suppression Via a Self-Supervised Deep Learning Scheme
Bibliographic record
Abstract
Summary Coherent noise attenuation is an essential step in seismic data processing to improve data quality and signal-to-noise ratio. The use of deep learning based approaches for noise suppression has grown throughout the last five years due to neural networks strength in pattern recognition tasks and their low computation cost, i.e. fast application during the inference stage. A limitation of the majority of such procedures is their requirement for noisy-clean pairs of data for training. Here, we propose the use of self-supervised procedure, namely, Structured Noise2Void, which has no such requirements. Through the inclusion of a noise mask, the coherency of noise is suppressed by randomising the noise, allowing the network to learn how to predict only the signal component of a sample’s value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate trace-wise coherent noise. In the synthetic example, noise was injected into ten random traces, which showed no notable indication of their previously noisy state after denoising. In the field data, some locations already exhibited trace-wise coherent noise. After application of the trained network, the noise on these traces was drastically reduced resulting in a notable continuation in the seismic wave’s first arrival.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".