Earthquake Depths, Focal Mechanisms, and Stress in the Lower St. Lawrence Seismic Zone
Bibliographic record
Abstract
Abstract We examine earthquake hypocenters, focal mechanisms, and the state of tectonic stress in the Lower St. Lawrence Seismic Zone (LSZ), a paleorift zone in eastern Canada. The largest earthquake recorded in the region is the 1999 Côte–Nord MN 5.1, which was followed by ∼80 aftershocks of MN>1. It is not known if the region is capable of producing hazardous Mw>6 earthquakes, similar to the Charlevoix Seismic Zone ∼250 km upriver. Focusing on 2015–2020, we apply a machine-learning-based phase picker to detect 72 earthquakes in addition to the 150 catalog earthquakes in the same region over this time span. We produce an updated 1D, gradient velocity model via a Monte Carlo search using a uniform VP/VS=1.77, which we computed with the Wadati method. We refine hypocenter estimates using the triple-difference method, with sP depth phases as additional constraints on earthquake depth. We estimate focal mechanisms for >100 earthquakes with automatically picked P-wave first motions and absolute value P-SV-SH amplitude ratios, and we use the focal mechanisms to invert for the state of tectonic stress. Grid searches and Bayesian analysis allow for robust uncertainty estimates of focal mechanisms, which in turn allow for uncertainty estimates of the stress tensor. The recovered west-northwest–east-southeast σ1 is consistent with previous estimates and with a stress tensor controlled by glacial isostatic adjustment, although a contrast between deep and shallow focal mechanisms suggests that these stresses may be concentrated in the lower crust. Epicenter lineations up to ∼40 km long may be indicative of sizable faults in the LSZ capable of generating Mw>6 earthquakes, but hypocenter and focal mechanism uncertainties are too high to say so definitively, thus pointing to a need for denser station coverage, including ocean-bottom seismometers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".