Application of constrained polarization filtering for surface-wave mitigation: Three case studies
Bibliographic record
Abstract
ABSTRACT We applied constrained polarization filtering (CPF) to surface-wave mitigation on data sets from different geologic settings. The method derives from the application of polarization filtering in the time-frequency (t-f) domain and introduces new constraints to effectively detect and mitigate surface waves while protecting the signal. We use these constraints that we derive from velocity, amplitude, time, and frequency information to delineate the t-f region dominated by surface-wave noises. Then, we restrict the application of polarization filtering to this region to avoid damaging the signal. Straightforward application of polarization filtering without these constraints results in ineffective filtering or damage to the signal, due to the complexity of surface-wave wavetrains. The performance of CPF with these various data sets is demonstrably superior compared to the unconstrained approach. There are some of the issues that may affect performance of the CPF, but they can be overcome.
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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) | 0.000 | 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 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".