Imaging mantle discontinuities using least squares Radon transform
Bibliographic record
Abstract
Differential traveltimes of SS precursors have been widely used to map large‐scale mantle structure and depths of discontinuities. Measurements are commonly made by stacking individual records to increase the signal‐to‐noise ratio pertaining to these mild reflections. However, ray parameters of the SS precursors are typically less well constrained and undesired seismic arrivals with vastly different slownesses (for example, scattered waves) could potentially contaminate the time domain stacks. To overcome these pitfalls, we introduce a processing scheme based on the Radon transform and well‐constrained inversions. Our method is particularly effective in suppressing background noise, constraining differential ray parameter and traveltime, and detecting weak reflected or converted phases. We apply the Radon‐based method to delineate the discontinuity structure beneath the northeastern Pacific Ocean and the northwestern Canada. An elevated 410‐km discontinuity and a thickened transition zone (252 km) are observed beneath the northern British Columbia, which may be caused by remnant oceanic lithosphere from the subduction of Kula‐Farallon plate under North America. A thin transition zone is identified beneath the northern Pacific Ocean and its presence is supported by a low shear‐velocity anomaly from recent tomographic models. The improved accuracy and resolution using the least squares Radon transform also offer clear evidence for a regional 520‐km discontinuity and several mild reflectors in the depth ranges of 250–330 km and 900–1200 km. We do not observe a 220‐km discontinuity beneath the study region.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".