Modeling of ionospheric magnetic field perturbations induced by earthquakes
Bibliographic record
Abstract
The objective of this study is to present a simple coupled seismo‐ionospheric model to account for magnetic field perturbations in response to geophysical phenomena such as Earthquakes or tsunamis. For this purpose, we include a simple gravity wave model in the two dimensional mid latitude ionosphere model SAMI2. Following an earthquake, the disturbances in the neutral atmosphere density and velocity associated with gravity waves propagate vertically upward up to ionospheric altitudes 150–350 km, where significant coupling between the neutral atmosphere and the ionosphere occurs. As a result, a rapid variation in the ionospheric plasma density and temperature is observed associated with the photoionization of the perturbed neutral density. This variation in the electron density leads in turn to a variation in the total electron content (TEC). Strong variations in the TEC are observed close to the epicenter which correspond to large plasma density perturbations in this region associated with the ion acoustic mode. The calculation of magnetic perturbations is based on a set of reduced MHD equations. These are solved numerically using the finite element code TOPO coupled with the mid latitude ionospheric model SAMI2. The coupling between the two codes accounts for the variations in neutral gas density and the collisional drag force between ions and neutrals. Two wave modes are considered: the shear Alfvén mode and the compressional mode. The results of the magnetic field perturbations and of ions' velocity perturbations associated with the shear Alfvén and the compressional modes are presented. Variations in the TEC values are also computed for the compressional mode by taking into account the density perturbations transverse to the magnetic field. The results representing TEC perturbations associated with the compressional mode show strong variations at high latitudes both as a function of time and latitude.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".