Estimation of Quality Factor (Qβ) Using Accelerograms of Ahar-Varzaghan Earthquakes, Northwestern Iran
Bibliographic record
Abstract
High-frequency strong-motion data of two recent major earthquakes in the Ahar-Varzaghan region, Northwestern Iran, have been used to determine shear-wave quality factor ( Q β ( f )) and seismic source parameters. Data from a local array of 12 stations, two main shocks (Ahar-Varzaghan doublet earthquakes ( M w 6.4 and 6.3) on August 11, 2012), and 38 aftershocks of magnitude 4.1 – 5 were analyzed. The classic Brune model is used to predict the shape of the source spectrum and to provide scaling relationships between spectral and source parameters. In order to obtain reliable estimates of the source spectrum, the effects of attenuation need to be estimated and corrected. By using an inversion algorithm in this work, an average relation in the form Q β = (114 ± 21) f (0.90 ± 0.07) is obtained. The best fit theoretical spectrum provides final values of source parameters, i.e. seismic moment M o and corner frequency f c as 3.19 × 10 25 dyne cm and 0.69 Hz, respectively, for the first event. Obtained Q(f) relationship suggests a low Q o value (< 200) and a high n value (> 0.8) for high heterogeneous, tectonically and seismically active regions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".