Single-station SVD-based polarization filtering of ground roll: Perfection and investigation of limitations and pitfalls
Bibliographic record
Abstract
ABSTRACT The advent of single receiver point, multi-component geophones has necessitated that ground roll be removed in the processing flow rather than through acquisition design. A wide class of processing methods for ground-roll elimination is polarization filtering. A number of these methods use singular value decomposition (SVD) or some related transformations. We focus on a single-station SVD-based polarization filter that we consider to be one of the best in the industry. The method is comprised of two stages: (1) ground-roll detection and (2) ground-roll estimation and filtering. To detect the ground roll, a special attribute dependent on the singular values of a three-column matrix formed by a sliding time window is used. The ground roll is approximated and subtracted using the first two eigenimages of this matrix. To limit the possible damage to the signal, the filter operates within the record intervals where the ground roll is detected and within the ground-roll frequency bandwidth only. We improve the ground-roll detector to make it theoretically insensitive to ambient noise and more sensitive to the presence of ground roll. The advantage of the new detector is demonstrated on synthetic and field data sets. We estimate theoretically and with synthetic data the attenuation of the underlying reflections that can be caused by the polarization filter. We show that the underlying signal always loses almost all the energy on the vertical component and on the horizontal component in the ground-roll propagation plane and within the ground-roll frequency bandwidth. The only signal component, if it exists, that can retain a significant part of its energy is the horizontal component orthogonal to the above plane. When 2D 3C field operations are conducted, the signal particle motion can deviate from the ground-roll propagation plane and can therefore retain some of its energy due to a set of offline reflections. In the case of 3D 3C seismic surveys, the reflected signal always deviates from the ground-roll propagation plane on the receiver lines that do not contain the source. This is confirmed with a 2.5D 3C synthetic data set. We discuss when the ability of the filter to effectively subtract the ground roll may, or may not, allow us to ignore the inevitable harm that is done to the underlying reflected waves.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".