The Investigation of Tropospheric Changes with GNSS: A study on 6 February 2023 Kahramanmaraş Earthquake Sequence
Bibliographic record
Abstract
The earthquakes that occurred in Kahramanmaraş on February 6, 2023, are among the significant seismic events in Turkey. Recorded at moment magnitudes of 7.8 and 7.6 in ten hours on East Anatolian Fault Zone (EAFZ), these earthquakes resulted in extensive destruction and loss of lives in the region. The effects of these earthquakes have been actively studied following the events, utilizing geodetic measurement techniques, particularly GNSS measurements, which are commonly employed in earthquake studies for determining tectonic movements and crustal deformations. As known, GNSS signals pass through significant atmospheric layers, namely the ionosphere and troposphere, before reaching the Earth's surface, and the influence of these atmospheric layers is evident in the results due to various error sources within these layers. One of the main limiting factors in studies such as determining crustal movements is the influence of the troposphere, as surface velocities are on the order of a few mm/yr and require high accuracy (at the mm level). In this study, changes in the troposphere during the earthquakes on February 6, 2023, were investigated using tropospheric zenith delays (Zenith Total Delay - ZTD) computed from GNSS observations. The results indicate the presence of zenith tropospheric delay anomalies at stations close to the fault rupture during and after the main shock, while no such anomalies were observed at distant stations from the fault rupture zone. This finding indicates a relationship between earthquakes and changes occurring in the troposphere.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".