Ground roll removal from a deep 3-D re\nection dataset using Physical Wavelet Frame
Bibliographic record
Abstract
Deep crustal seismic re\nection datasets contaminated with ground roll challenge processors to try and remove the high amplitude, low frequency dispersive waves while leaving low frequency deep re\nections intact. Through the use of Physical Wavelet Frame Denoising (PWFD), a frame theory 2-D-wavelet-based method designed to extract hyperbolae from shot gathers, the ground roll is suppressed signicantly from a deep 3-D re\nection dataset collected in the Pincher Creek region of south-western Alberta, Canada. This dataset, which represents the rst attempt in Canada to image deep Precambrian re ectors using 3-D re\nection techniques, contains signif-icant ground roll which obscures key re\nections. While conventional ground roll removal techniques are unable to remove the coherent noise without also altering the deep re\nections, PWFD retains deep re\nections and so facilitates velocity analyses and improves the stacked results. In this paper, we demonstrate the successful use of PWFD in the removal of ground roll from deep seismic re ection data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".