Denoising of Seismic Signals Through Wavelet Transform Based on Entropy and Inter-scale Correlation Model
Bibliographic record
Abstract
For effective removal of noises in seismic signals, this paper proposes an adaptive threshold denoising algorithm that integrates wavelet transform with entropy and inter-scale correlation (EIS) model.Firstly, noisy signals were decomposed by discrete wavelet transform, the highfrequency sub-bands on each scale were divided into equal subintervals, and the wavelet entropies of the subintervals were computed one by one.Secondly, the correlation coefficients of sampling points on each scale were calculated, and then compared with the high-frequency coefficients at corresponding positions.The comparison results, coupled with the wavelet entropies, were determine the noise variance of high-frequency sub-bands on each scale.Finally, the signals were reconstructed from the above results according to the new threshold function and the self-adaptive threshold rule.The experimental results show that our method outperformed several popular denoising approaches in terms of signal-to-distortion ratio (SDR), signal-to-noise ratio (SNR) and mean squared error (MSE).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".