Uncertainties in earthquake source spectrum estimation using empirical Green functions
Bibliographic record
Abstract
We analyze the problem of reliably estimating uncertainties of the earthquake source spectrum and related source parameters using Empirical Green Functions (EGF). We take advantage of the large dataset available from 10 seismic stations at hypocentral distances (10 km < d < 50 km) to average spectral ratios of the 2001 M5.1 Anza earthquake and 160 nearby aftershocks. We estimate the uncertainty of the average source spectrum of the M5.1 target earthquake by performing propagation of errors, which, due to the large number of EGFs used, is significantly smaller than that obtained usingasingle EGF. Our approach provides estimates of both the earthquake source spectrum and its uncertainties, plus confidence intervals on related source parameters such as radiated seismic energy or apparent stress, allowing the assessment of statistical significance. This is of paramount importance when comparing different sized earthquakes and analyzing source scaling of the earthquake rupture process. Our best estimate of radiated energy for the target earthquake is 1.24 × 1011 Joules with 95% confidence intervals (0.73 × 1011 , 2.28 × 1011). The estimated apparent stress of 0.33 (0.19, 0.59) MPa is relatively low compared to previous estimates from smaller earthquakes (1MPa) in the same region.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".