Adaptive dictionary identification framework and its application to sparsity-optimized harmonic noise separation
Bibliographic record
Abstract
ABSTRACT Coherent noise separation stands as a crucial step in seismic data processing. The morphological component analysis (MCA)-based separation method, which treats coherent noise and signal as distinct components and represents them sparsely with dictionaries, has been widely adopted for noise suppression. Typically, constructing effective fixed dictionaries for MCA-based methods involves necessary expert knowledge to meticulously select appropriate transform basis functions from an extensive dictionary library and fine-tune their parameters. To reduce time consumption and ensure optimal dictionary construction, we introduce an adaptive framework for identifying optimal dictionaries used in MCA-based coherent noise separation. Initially, we define a fixed dictionary library comprising dictionaries constructed using various transform basis functions with their corresponding parameters. Subsequently, we formulate a relative sparsity minimization problem (RSMP) to identify the optimal fixed dictionaries that minimize relative sparsity within this predefined library. Finally, we design a genetic algorithm to solve RSMP. The identified dictionaries are then applied to MCA-based coherent noise separation. Synthetic and field data examples demonstrate the effectiveness of our method.
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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) | 0.000 | 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 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".