Lateral Variations of Attenuation in the Crust of Alaska Using <i>Lg</i> <i>Q</i> Tomography
Bibliographic record
Abstract
ABSTRACT We have conducted a crustal seismic (QLg) attenuation tomography study across Alaska using recordings from the EarthScope USArray from 2014 to 2019. The resolving power of the inversion is 150 × 150 km for most of Alaska, and it is 75 × 75 km in central and southern Alaska. Numerous fault systems and high mountain ranges are present across Alaska and accommodate compression in the north–south direction and shearing of southern Alaska toward the west. These mountain ranges include the Brooks range in the north, the Alaska range in central Alaska, and the Aleutian range in the southwest. The average LgQ for all of Alaska is significantly higher than in the western United States and Canada. This lower average attenuation impacts seismic hazard estimates for the region. According to the tomographic results, we see a significant variation of the QLg values from low to high across the southern part of the Brooks range. Also, we found higher attenuation in the southeast region of Alaska, where the Wrangell volcanoes are located. Moreover, we see an area of lower attenuation associated with weak frequency dependence in the south-central region of Alaska next to Anchorage. Another anomaly with lower attenuation can be seen extending from central Alaska to southeast Alaska, possibly associated with the Yukon–Tanana terrane. There are a few areas like southwest Alaska associated with the Togiak terrane and an area next to Fairbanks in Alaska’s interior that shows lower attenuation with lower frequency dependence and higher attenuation with higher frequency dependence, respectively, for low frequencies up to 3 Hz. Our model’s highest η zones (η≳95) are mostly confined to major tectonic terranes and other major tectonic elements such as faults and fractures. Regional variations in crustal attenuation can impact local seismic hazard estimates if incorporated into the hazard analysis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".