Deriving the geomagnetically induced electric field at the Earth’s surface from the time derivative of the vertical magnetic field
Bibliographic record
Abstract
We present a new method for estimating the geomagnetically induced electric field at the Earth’s surface directly from the time derivative of the vertical magnetic field, without any need for additional information about the Earth’s electric conductivity. This is a simplification compared to the presently used calculation methods, which require both the magnetic variation field and ground conductivity model as input data. The surface electric field is needed e.g. in modeling Geomagnetically Induced Currents (GIC) that flow in man-made conductor systems, such as gas and oil pipelines or high-voltage power grids. We solve the induced electric field directly from Faraday’s law, by representing the magnetic variation field in terms of external equivalent current and taking time derivative of the associated vector potential. This gives an approximative solution, where the divergence-free part of the electric field is reproduced accurately (at least in principle), but the curl-free part related to lateral variations in ground conductivity is completely neglected. We test the new calculation method with several realistic models of typical ionospheric current systems, as well as actual data from the Baltic Electromagnetic Array Research (BEAR) network. We conclude that the principle of calculating the (divergence-free part of the) surface electric field from time derivative of the vertical magnetic field is sound, and the method works reasonably well also in practice. However, practical applications may be rather limited as the method seems to require data from a quite dense and spatially extended magnetometer network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".