A Regional Seismic Travel Time Model for North America
Bibliographic record
Abstract
Abstract : Monitoring seismic events at ever-lower magnitude thresholds requires the utilization of seismic stations that are close to the event. The proliferation of seismic stations across the globe results in lower seismic detection thresholds because more stations are likely to be at regional distance to an event. However, utilization of arrival time data at regional distances often results in degraded location accuracy, because large variations in regional crust and upper-mantle structure can result in large travel time prediction errors. We have completed the data compilation needed to extend RSTT tomography to North America. That effort includes integration into our database of data provided by the NEIC and picks provided by the Array Network Facility for USArray stations. The integrated data have been relocated and epicenter accuracy criteria applied. Pick outliers have been identified based on overall statistics and comparison with neighboring data. Summary rays for the Pn data (Figure 3) provide excellent ray coverage across western North America, good coverage across the eastern U.S., poor coverage across central and eastern Canada. Preliminary tomographic results use a starting model that is based on CRUST2.0. We find that modification to CRUST2.0 are needed. Nonetheless, preliminary tomographic results are consistent with known velocities for major tectonic provinces. Tests of travel-time prediction based on a validation data set (Figure7) show approximately 25% reduction in the standard deviation of residuals. 2010 Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies 316
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".