Crustal anisotropy beneath Hudson Bay from ambient noise tomography: Evidence for post‐orogenic lower‐crustal flow?
Bibliographic record
Abstract
The crust underlying Hudson Bay, Canada records a long and complex tectonic history. In this study, we investigate this region using tomographic inversion based on continuous ambient noise recordings from 37 broadband seismograph stations that encircle Hudson Bay. The ambient noise data were processed to obtain group‐velocity dispersion measurements from 10–35 s period, which were inverted using an algorithm that incorporates the effects of anisotropy. This work is among the first in which ambient noise data have been used to investigate azimuthal anisotropy. The inversion method uses smoothing and damping to regularize the solution; due to the significantly increased number of model parameters relative to the isotropic case, we performed a careful analysis for parameter selection to determine whether “leakage” occurs between isotropic and anisotropic model parameters. We observe a robust pattern of anisotropic fast directions in the mid‐crust that are consistent with large‐scale tectonic trends based on magnetic‐anomaly patterns. In particular, a distinctive double‐indentor shape for the Superior craton is clearly expressed in both data sets. This pattern breaks down deeper in the crust, suggesting that some degree of lithospheric decoupling in the lower crust, such as channel flow, occurred during orogenesis. Given regional evidence for vertically coherent deformation in the crust and underlying mantle, we interpret this pattern in the lower crust as a tectonic overprint that post‐dates the main phase of Trans‐Hudson deformation. At most levels in the crust, we observe a profound change in direction of anisotropic fast direction across an inferred suture beneath Hudson Bay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
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".