Attenuation and Source Characteristics of the 23 June 2010 M 5.0 Val-des-Bois, Quebec, Earthquake
Bibliographic record
Abstract
The M 5.0 23 June 2010 Val-des-Bois, Quebec, earthquake produced a rich instrumental and felt ground-motion database. We use instrument-corrected response spectra and Fourier amplitude data from 120 stations, at distances from 60 to 1,000 km, to examine the attenuation and source characteristics of this important event. The Val-des-Bois earthquake produced relatively large response spectral amplitudes at distances less than 200 km, greater than predicted by most recent ground-motion prediction equations (GMPEs) (including the Atkinson and Boore 2006 equations). By contrast, reported intensities at regional distances tended to be smaller than predicted by intensity GMPEs (Atkinson and Wald 2007), although they were high in the epicentral area. From recent moderate earthquakes in eastern North America (ENA) (2010 Val-des-Bois and the 2005 Riviere du Loup event), we have learned that amplitudes at near distances are not well-predicted by average attenuation shapes drawn to pass through regional observations. To infer the source spectrum or near-source motions, we suggest the use of seismic moment as a constraint on the level of the source spectrum. Using Q -corrected observations to deduce the source-spectral shape, and the known seismic moment to fix its absolute amplitude level, we obtain an apparent source spectrum for the Val-des-Bois earthquake. The Val-des-Bois source spectrum is well described by a Brune model with a stress drop of 250 bars. Future work will focus on resolving near-source attenuation issues to provide better GMPEs for ENA for all magnitudes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".