Strong random noise attenuation by shearlet transform and time-frequency peak filtering
Bibliographic record
Abstract
ABSTRACT Directional wavelet transforms combined with coefficient thresholding are very competitive in denoising seismic signals. However, these techniques struggle when the coefficients of signal and noise have comparable magnitudes. To better address this problem, we have developed an improvement to this method by applying time-frequency peak filtering (TFPF) to the directional wavelet coefficients. TFPF consists of computing the instantaneous frequency of a frequency-modulated analytic signal. The use of a longer or shorter smoothing window helps to emphasize either signal or remove random noise. In our method, we use the shearlet transform as a directional wavelet transform and estimate signal dips based on the cumulative energy in each decomposition direction. TFPF is then applied to the fine-scale wavelet coefficients to enhance signal and remove high-frequency noise. Coefficient thresholding is applied to all other scales. Experimental results demonstrate that our algorithm can effectively eliminate strong random noise and preserve events of interest.
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 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) | 0.001 | 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 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".