Linearized inverse scattering of teleseismic waves for anisotropic crust and mantle structure: 2. Numerical examples and application to data from Canadian stations
Bibliographic record
Abstract
The objective of this study is to characterize elastic properties, including anisotropy, at the base of the crust and uppermost mantle using the Moho PMs phase recorded in the teleseismic P coda at Canadian stations. We use linearized inverse scattering and singular value decomposition to identify those parameter combinations to which idealized teleseismic data sets are most sensitive. Five to seven independent parameter combinations are likely to be resolvable, one of which is sensitive to isotropy, whereas the remainder quantify different harmonic orders (1θ, 2θ, 3θ) of back azimuthal response. Aside from the isotropic component which is resolved only by P‐SV interactions, P‐SV and P‐SH conversions exhibit a redundant sensitivity to model parameters for uniform back‐azimuthal sampling. We use parameter combinations from the idealized, uniform back‐azimuthal data distribution to compare Moho anisotropy at 25 broadband stations on the Canadian landmass. The isotropic component dominates at all stations and corresponds to shear velocity contrasts ranging between 10 and 35%. Perturbations to anisotropic material property parameters are more modest, generally between 3 and 7% when consistent between SV and SH responses and in many cases suggest an anisotropic lower crust. Inconsistent responses may manifest contamination by lateral heterogeneity, upper crustal reverberations, or pervasive crustal anisotropy leading to shear wave splitting.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".