Shear Velocity Model of Alaska Via Joint Inversion of Rayleigh Wave Ellipticity, Phase Velocities, and Receiver Functions Across the Alaska Transportable Array
Bibliographic record
Abstract
Abstract Through the Alaska Transportable Array deployment of over 200 stations, we create a 3‐D tomographic model of Alaska with sensitivity ranging from the near surface (<1 km) into the upper mantle (~140 km). We perform a Markov chain Monte Carlo joint inversion of Rayleigh wave ellipticity and phase velocities, from both ambient noise and earthquake measurements, along with receiver functions to create a shear wave velocity model. We also use a follow‐up phase velocity inversion to resolve interstation structure. By comparing our results to previous tomography, geology, and geophysical studies we are able to validate our findings and connect localized near‐surface studies with deeper, regional models. Specifically, we are able to resolve shallow basins, including the Copper River, Cook Inlet, Yukon Flats, Nenana, and a variety of other shallower basins. Additionally, we gain insight on the interaction between the upper mantle wedge, asthenosphere, and active and nonactive volcanism along the Aleutians and Denali volcanic gap, respectively. We observe thicker crust beneath the Brooks Range and south of the Denali fault within the Wrangellia Composite Terrane and thinner crust in the Yukon Composite Terrane in interior Alaska. We also gain new perspective on the Wrangell Volcanic Field and its interaction between surrounding asthenosphere and the Yakutat Terrane.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".