Earthquake Magnitude and Lg Q Variations between the Grenville and Northern Appalachian Geologic Provinces of Eastern Canada
Bibliographic record
Abstract
ABSTRACT This article assesses the ability of regionally specific, frequency-dependent crustal attenuation (1/Q) to reduce mean magnitude discrepancies between seismic stations in the northern Appalachian and Grenville provinces (NAP and GP) of Canada. LgQ(f) is an important parameter in ground-motion models used in probabilistic seismic hazard analysis. Discrepancies in regional magnitude estimates have long been noted to exist between stations in the two provinces for common event origins. Such discrepancies could arise from systematic site condition variations between the geologic provinces or from varying crustal attenuative properties. To evaluate the effect of frequency-dependent anelastic attenuation, LgQ(f) on estimated magnitudes, we analyze Lg amplitudes from >6000 waveforms recorded by Grenville and northern Appalachian receivers from 420 natural earthquakes of MN magnitude 3–5.6. Waveform analysis is strictly limited to analyst-reviewed, vertical-component waveforms in which Lg is clearly identified, ensuring that the datasets exhibit dominant, high-frequency energy in the Lg velocity window. LgQ(f) is found to be higher in the GP than in the northern Appalachians. In the Grenville, Q(f)=761(±145)f0.25(±0.014), and in the northern Appalachians, attenuation is higher: Q(f)=506(±172)f0.33(±0.310). Earthquake magnitude determined using the peak amplitude of the Lg phase (mbLg) for eastern Canada is corrected to incorporate the frequency-dependent, regionally specific LgQ(f) determined in this study. Using the new LgQ(f) values diminishes and nearly resolves magnitude discrepancies between the provinces. Correcting regional magnitude discrepancies between provinces is critical for reliable regional seismic hazard estimates because magnitude error in a particular region could lead to increased uncertainty in seismic hazard models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".