Using relative event locations of swarms of small earthquakes to look for seismically active structures in Northeastern North America
Bibliographic record
Abstract
<!--!introduction!--> Swarms of small earthquakes and microearthquakes have been observed in several different places in the northeastern U.S. and southeastern Canada over the past several decades. The modern seismic network that has operated during this time has allowed earthquakes to magnitudes below M 1.0 in these swarms to be detected and located. A relative earthquake location method using waveform crosscorrelations allows for very precise relative event locations to be computed, which can be used to look for spatial trends in the earthquake swarms. Absolute events locations are best determined using event recordings on portable seismic instruments in the epicentral area. Relative and absolute location analyses of several swarms show a consistent pattern of the swarm seismicity aligning along known or suspected preexisting faults. These swarms, located in New York, Connecticut, Maine and New Brunswick, may be indicating which pre-existing structures in Northeastern North America might be seismically active in the future. Whether or not these structures could host a strong earthquake in the future is not clear from the data. Even so, the analyses suggest that studies of the relative event locations of future swarms of small earthquakes and microearthquakes may help clarify the picture of which pre-existing zones of weakness may pose the greatest seismic hazard in the region.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".