Ground-roll attenuation through quaternionic inversion with sparsity constraints
Bibliographic record
Abstract
Surface waves, such as ground roll, are a major source of coherent noise in land seismic data, and its attenuation is still a challenge during processing. Even with the most simplifying assumption of a homogenous half-space, one can show that ground roll displacements in x and z components of a vectorvalued dataset are related. This paper discusses how these displacements can be integrated into a quaternion array in the frequency-space domain and aim at exploiting the mutual information between these signals. One can use the quaternion array to model surface waves using a least-squares inversion methodology with sparsity constraints and then follow with a subtraction strategy to attenuate the surface waves from the multicomponent data. The quaternionic approach, when contrasted with its scalar/componentwise counterpart, could provide better ground roll attenuation as presented with a test using a 2C-2D field data from Alberta, Canada. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: Poster Station 13 Presentation Type: Poster
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".