Temporal properties of aftershock sequences of large earthquakes in Iran - Analysis of primary and secondary aftershocks of the Ezgeleh sequence
Bibliographic record
Abstract
In this study, the decay of earthquake aftershock sequences of some major earthquakes in different tectonic regimes in the Iranian plateau is discussed. The studied earthquakes are Rigan [2010], Ahar-Varzaghan [2012], Goharan [2013], Sefidsang [2017] and Ezgeleh [2017]. The spatial and temporal windows are considered based on the method proposed by Gardner and Knopoff [1974] to compute decay parameters for each sequence. The decay rates of sequences were compared to well-known models to find the best fit for each sequence. The results showed that the modified Omori is the best fit for Ahar-Varzaghan and Ezgeleh sequences, for Rigan and Sefidsang sequences the modified Omori and the Kisslinger ones found as the best fits. The values of the p parameter of the Reasenberg and Shcherbakov models were larger compared to the Omori model, but the parameter of the Kisslinger model was slightly smaller compared to the Omori one. The c parameter showed an inverse relation to the threshold magnitude. The correlation between the p and c parameters and also the and the Gutenberg and Richter (G-R) parameters were investigated. In addition, we made use of a graphical method to analyze the seismic sequence of the Ezgeleh earthquake during 13 months after the main event. The graphical method was successful to estimate the occurrence of an event with an approximate magnitude of M=6.4 in the sequence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".