Using a Large‐<i>n</i> Seismic Array to Explore the Robustness of Spectral Estimations
Bibliographic record
Abstract
Abstract Spectral analysis is widely used to estimate and refine earthquake source parameters such as source radius, seismic moment, and stress drop. This study aims to quantify the precision of the single spectra and empirical Green's function spectral ratio approach using the Large‐n Seismic Survey in Oklahoma (LASSO) array. The dense station coverage in an area of local saltwater disposal offers a unique opportunity to observe and quantify radiation pattern effects and subsequent precision of spectral estimates of small earthquakes (M < 3). The results suggest that the precision of source properties estimated from direct phase arrivals for arrays with less than 20 stations should be assumed to be not less than 30% and could be as high as 150% if less than five stations are used. Furthermore, we do not see clear evidence for, or against, a scaling of stress drop with magnitude of small earthquakes (M < 3) as observed by other studies.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".