Applying inversion techniques to derive source currents and geoelectric fields for geomagnetically induced current calculations
Bibliographic record
Abstract
Abstract. This research focuses on the inversion of geomagnetic variation field measurement to obtain source currents in the ionosphere. During a geomagnetic disturbance, the ionospheric currents create magnetic field variations that induce geoelectric fields, which drive geomagnetically induced currents (GIC) in power systems. These GIC may disturb the operation of power systems and cause damage to grounded power transformers. The geoelectric fields at any location of interest can be determined from the source currents in the ionosphere through a solution of the forward problem. Line currents running east–west along given surface position are postulated to exist at a certain height above the Earth's surface. This physical arrangement results in the fields on the ground having the magnetic north and down components, and the electric east component. Ionospheric currents are modelled by inverting Fourier integrals (over the wavenumber) of elementary geomagnetic fields using the Levenberg–Marquardt technique. The output parameters of the inversion model are the current strength, height and surface position of the ionospheric current system. A ground conductivity structure with five layers from Quebec, Canada, based on the Layered-Earth model is used to obtain the complex skin depth at a given angular frequency. This paper presents preliminary and inversion results based on these structures and simulated geomagnetic fields. The results show some interesting features in the frequency domain. Model parameters obtained through inversion are within 2% of simulated values. This technique has applications for modelling the currents of electrojets at the equator and auroral regions, as well as currents in the magnetosphere.
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) | 0.000 | 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) | 0.003 | 0.001 |
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".