Coherent Noise Suppression Via a Self-Supervised Deep Learning Scheme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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 it