Random and coherent noise attenuation by empirical mode decomposition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We have devised a new filtering technique for random and coherent noise attenuation in seismic data by applying empirical mode decomposition (EMD) on constant-frequency slices in the frequency-offset (f-x) domain and removing the first intrinsic mode function. The motivation behind this development is to overcome the potential low performance of f-x deconvolution for signal-to-noise enhancement when processing highly complex geologic sections, data acquired using irregular trace spacing, and/or data contaminated with steeply dipping coherent noise. The resulting f-x EMD method is equivalent to an autoadaptive f-k filter with a frequency-dependent, high-wavenumber cut filtering property. Removing both random and steeply dipping coherent noise in either prestack or stacked/migrated sections is useful and compares well with other noise-reduction methods, such as f-x deconvolution, median filtering, and local singular value decomposition. In its simplest implementation, f-x EMD is parameter-free and can be applied to entire data sets without user interaction.
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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 | 0.000 |
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 it