Seismic Signal Matching and Complex Noise Suppression by Zernike Moments and Trilateral Weighted Sparse Coding
Bibliographic record
Abstract
Noise in field seismic data is usually complicated. Most signal-to-noise (S/N) ratio enhancement methods are designed for some specific noise type and they will become less effective when dealing with complex noise. In this article, we propose to use a trilateral weighted sparse coding (TWSC) scheme within the block matching framework for complex noise attenuation. Block matching-based approaches take the repetitive nature of effective signals into account which facilitates the S/N ratio enhancement. Block matching requires the identification of similar signal patches through a data set. This becomes more challenging for low-quality data. Thus, the signal matching criterion is of great importance. The Euclidean distance is the most widely used criterion for similarity measurement in block matching. It becomes a bottleneck for matching low-quality data or for data with rotations. To overcome this obstacle, we modify the Euclidean distance criterion by computing distances using Zernike moments which are rotation invariant and add noise robustness. This in turn benefits the subsequent filtering stage by TWSC, which uses two weight matrices to characterize the complex noise properties, and a third matrix to characterize the sparsity priors of signal. The improved sparse coding model is solved by the alternating direction method of multipliers. Tests on synthetic and several field data sets show that the proposed strategy achieves better performance in dealing with complex noise.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".