Determination of Stress Parameters for Eight Well-Recorded Earthquakes in Eastern North America
Bibliographic record
Abstract
We determined the stress parameter, Δσ, for the eight earthquakes stud- ied by Atkinson and Boore (2006), using an updated dataset and a revised point- source stochastic model that captures the effect of a finite fault. We consider four geometrical-spreading functions, ranging from 1=R at all distances to two- or three- part functions. The Δσ values are sensitive to the rate of geometrical spreading at close distances, with 1=R 1:3 spreading implying much higher Δσ than models with 1=R spreading. The important difference in ground motions of most engineering con- cern, however, arises not from whether the geometrical spreading is 1=R 1:3 or 1=R at close distances, but from whether a region of flat or increasing geometrical spreading at intermediate distances is present, as long as Δσ is constrained by data that are largely at distances of 100 km-800 km. The simple 1=R model fits the sparse data for the eight events as well as do more complex models determined from larger datasets (where the larger datasets were used in our previous ground-motion predic- tion equations); this suggests that uncertainty in attenuation rates is an important com- ponent of epistemic uncertainty in ground-motion modeling. For the attenuation model used by Atkinson and Boore (2006), the average value of Δσ from the point- source model ranges from 180 bars to 250 bars, depending on whether or not the stress parameter from the 1988 Saguenay earthquake is included in the average. We also find that Δσ for a given earthquake is sensitive to its moment magnitude M, with a change of 0.1 magnitude units producing a factor of 1.3 change in the derived Δσ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".