Seismic Response of Cook Inlet Sedimentary Basin, Southern Alaska
Bibliographic record
Abstract
Abstract Cook Inlet fore‐arc basin in south‐central Alaska is a large, deep (7.6 km) sedimentary basin with the Anchorage metropolitan region on its margins. From 2015 to 2017, a set of 28 broadband seismic stations was deployed in the region as part of the Southern Alaska Lithosphere and Mantle Observation Network (SALMON) project. The SALMON stations, which also cover the remote western portion of Cook Inlet basin and the back‐arc region, form the basis for our observational study of the seismic response of Cook Inlet basin. We quantify the influence of Cook Inlet basin on the seismic wavefield using three data sets: (1) ambient‐noise amplitudes of 18 basin stations relative to a nonbasin reference station, (2) earthquake ground‐motion metrics for 34 crustal and intraslab earthquakes, and (3) spectral ratios (SRs) between basin stations and nonbasin stations for the same earthquakes. For all analyses, we examine how quantities vary with the frequency content of the seismic signal and with the basin depth at each station. Seismic waves from earthquakes and from ambient noise are amplified within Cook Inlet basin. At low frequencies (0.1–0.5 Hz), ambient‐noise ratios and earthquake SRs are in a general agreement with power amplification of 6–14 dB, corresponding to amplitude amplification factors of 2.0–5.0. At high frequencies (0.5–4.0 Hz), the basin amplifies the earthquake wavefield by similar factors. Our results indicate stronger amplification for the deeper basin stations such as near Nikiski on the Kenai Peninsula and weaker amplification near the margins of the basin. Future work devoted to 3D wavefield simulations and treatment of source and propagation effects should improve the characterization of the frequency‐dependent response of Cook Inlet basin to recorded and scenario earthquakes in the region.
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 | 0.000 |
| Scholarly communication | 0.000 | 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".