Effectively Distinguishing Blast and Earthquake Sources in Eastern Canada with Less Dense Seismic Station Coverage
Bibliographic record
Abstract
Eastern Canada, a stable continental region, has experienced magnitude 6+ earthquakes in the Western Quebec, Charlevoix, Lower St.Lawrence, and Northern Appalachians seismic zones. Distinguishing tectonic earthquakes from industrial blasts and processing low signal‑to‑noise ratio (SNR) waveforms remain challenging for maintaining seismic catalog accuracy in this region. Here, we introduce a convolutional neural network-based image classifier that processes denoised, three‑component spectrograms of approximately 80,000 labeled events (2000-2024) from the Canadian National Earthquake Database. Our final model achieves over 97\% accuracy in classifying earthquakes versus blasts under low‑SNR conditions. To address uneven station coverage, we apply Gaussian distance-based station weighting, producing intermediate prediction scores (0.3-0.7) to flag ambiguous cases (0 for blasts, 1 for tectonic earthquakes). This approach reduces false positives from machine‑learning phase pickers, enhances seismic catalog reliability, and offers a robust tool for regional hazard analysis with potential application in other intraplate settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".