Earthquake Catalog Processing and Swarm Identification for the Pacific Northwest
Bibliographic record
Abstract
Abstract The Pacific Northwest (PNW) of North America encompasses diverse tectonic settings that can produce damaging earthquakes near population centers. Seismicity in this region is often clustered into aftershock sequences and swarms, and their patterns and frequencies differ across subregions or tectonic regimes. Characterizing the seismicity of the PNW requires a catalog of observed earthquakes. Furthermore, applications with the catalog may require earthquake clusters to be identified and regarded separately. Unlike previous studies, we explicate how to overcome challenges when combining catalogs from different countries, particularly in accounting for duplicate events and other discrepancies. We apply this to merge authoritative catalogs for the United States and Canadian portions of the PNW, along with a third dataset with data quality measures. We also perform a window-based search for earthquake clusters, which then get labeled as possible or definite swarms or aftershock sequences. We further split the catalog into its two primary tectonic regimes. We then study the PNW catalog’s completeness, and the extent to which this varies between the northern and southern parts of the region. We provide a harmonized international PNW catalog with derived variables describing earthquake clustering and tectonic regimes. This entire processing pipeline has also been fully documented and is supported with software, enabling its use in other seismic regions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".