Mid‐Latitude Geoelectric Field Response in North China During the May 2024 Superstorm: Effects of Geological Heterogeneity
Bibliographic record
Abstract
Abstract Geoelectric field disturbances during geomagnetic storms pose growing risks to mid‐latitude power systems, yet direct observations remain limited. We report multi‐station, real‐time GIE observations from a new monitoring network in North China, comprising 22 geoelectric and 4 geomagnetic stations. This network captured detailed GIE responses during the May 2024 superstorm, providing rare mid‐latitude field measurements under extreme space weather conditions. Although geomagnetic field variations were relatively uniform across the region, the induced geoelectric fields exhibited significant spatial heterogeneity, primarily controlled by local subsurface conductivity. In mountainous regions such as the Qinling orogen, peak GIE amplitudes reached up to 1,500 mV/km—substantially higher than in surrounding basins and plains. Magnetotelluric (MT) impedance analysis reveals pronounced electrical anisotropy beneath these zones, which preferentially amplifies the north–south component of the GIEs. These findings offer key observational evidence linking subsurface structure to storm‐time GIE amplification, and have direct implications for GIC forecasting and infrastructure resilience in tectonically complex, mid‐latitude regions.
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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 | 0.001 | 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) | 0.001 | 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".