An overview of seismic attenuation in the Northern Appalachians Seismic Zone, New Brunswick and Nova Scotia
Bibliographic record
Abstract
In this study we investigated coda-wave attenuation (QC) from the northern Appalachian region of eastern Canada in the two provinces of New Brunswick and Nova Scotia. We used earthquake recordings from 8 broadband and 2 short period seismograph stations of the Canadian National Seismograph Network (CNSN) across the region. Our dataset is comprised of 476 earthquakes recorded between 1983 and 2021 with magnitudes ranging from 1.5 to 4.1, depths from 0 to 20 km (with the vast majority being <10 km) and epicentral distances of 5 to 100 km. This gives a total of 261 high signalto- noise (S/N) traces (S/N greater than or equal to 5.0) useful for QC calculation (with a maximum ellipse parameter, a2, of 100) across the region. Coda windows were selected to start at tc = 2tS (two times the travel time of the direct S wave), and were filtered at center frequencies of 2, 4, 8, 12 and 16 Hz. Our study reveals a consistent pattern. We find that in the northern New Brunswick, the lowest Q0 values (e.g., Q0 of 61) are at station KLN which is the closest station to the epicenter of the 1982 Miramichi earthquake (M 5.8). The highest Q0 values that we find (e.g., Q0 of 178) are at station GGN, located in the southern New Brunswick. Smaller Q0 values for stations in the north (closer to the Charlevoix-Kamouraska seismic zone or Miramichi source area) is explained by Jin and Aki's (1988) finding that Q0 is lower in the vicinity of large earthquakes. An average for all the data results in a Q relationship of QC = 99f0.96 for the frequency band of 2 to 16 Hz for the entire 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".