Source Characterization for Two Small Earthquakes in Dartmouth, Nova Scotia, Canada: Pushing the Limit of Single Station
Bibliographic record
Abstract
Abstract A pair of small earthquakes (MN 2.4 and 2.6, Earthquakes Canada) hit the city of Dartmouth, Nova Scotia, Canada, in early March 2020. The events were recorded by three seismic stations within 200 km, but only one station (HAL, <10 km) is close enough to offer high-quality broadband signals. In this study, we explore their source parameters using the nearest station through waveform modeling. A nearby quarry blast (MN 2.0) with known Global Positioning System coordinates is adopted as a reference for regional velocity model building and location calibration. We first build a half-space velocity model by estimating the P-S travel-time difference of the blast and determine the near-surface velocity through full-waveform modeling (i.e., comparing a set of synthetic waveforms with the observed blast). The velocity model is then used to evaluate the pair of earthquakes, in which waveform fitting and Rg/S amplitude ratios suggest source depths of ∼0.7 km. The epicenters of these two earthquakes are situated in a recently constructed commercial development. Lastly, single-station template matching finds no similar earthquakes near the hypocenters of the two events in the past decade and only three aftershocks in the following four months. Taking advantage of a ground-truth blast and waveform modeling, our study demonstrates the potential to construct a detailed regional velocity model and determine accurate earthquake source parameters in regions where only a single station is available.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".