Extrapolating strong ground motion of the Silakhor earthquake (ML 6.1), Iran, using the empirical Green's function (EGF) approach based on a genetic algorithm
Bibliographic record
Abstract
The main objectives of this article are to develop a technique to find source models that allow one to replicate observed strong ground motion records and to extrapolate strong ground motion synthesis to locations where strong motion was not recorded. A technique including the well known empirical Green’s function (EGF) approach along with a genetic algorithm is used, which allows the optimization of differences between the synthesized and observed ground shakings. The technique used is performed by comparing the elastic response spectra of observed seismograms at two stations with those of simulated data using the EGF method incorporating recorded aftershocks taken at each station. Moreover, a genetic algorithm approach is used to reduce differences between the simulated and recorded data in the form of elastic response spectra by changing the input parameters in the admissible ranges. To validate the proposed approach the three components of strong motion recorded at other stations were synthesized incorporating the input parameters obtained at previous stations. A comparatively good match of the simulated and recorded response spectra confirms the ability of the proposed technique to generate synthetic seismograms with suitable elastic response spectra.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".