Estimating Geoelectric Fields for Geoelectric Hazard Assessment: An Examination of Data and Models Within Complex Physiographic Zones
Bibliographic record
Abstract
During magnetic storms, time-varying geomagnetic fields induce geoelectric fields at the surface that produce geomagnetically induced currents (GICs) within power transmission systems.These GICs can permanently damage these systems, thus motivating research to understand how geoelectric fields behave during storm events.Geomagnetic field data obtained through the INTERMAGNET program are convolved with EarthScope USArray magnetotelluric impedances and impedances collected by Helmholtz Centre for Ocean Research Kiel (GEOMAR), FU Berlin, and GFZ Potsdam to estimate geoelectric variations during a magnetic storm.I consider a magnetic storm ranking G4 occurring between 22 June 2016 to 26 June 2016 recorded at the Brandon, Manitoba (BRD), Fredericksburg, Virginia (FRD), and San Juan (SJG) magnetic observatories.From this, I produce estimated geoelectric fields throughout the duration of a magnetic storm and examine these geoelectric fields across short geographic distances and within the same physiographic zone.This study shows that the geoelectric response of two sites within 200 km of one another can differ by up to two orders of magnitude (4484 mV/km at one site and 41 mV/km at another site 125 km away).I also examine how these geoelectric fields vary across a coastline in order to examine the geomagnetic coast effect's influence on geoelectric hazard assessment.From this, I demonstrate that the application of uniform 1-dimensional conductivity models of the subsurface to wide geographic regions is insufficient to predict the geoelectric hazard at a given site.This necessitates that an evaluation of the 3-dimensional conductivity distribution at a given location is necessary to produce a reliable estimation of how the geoelectric field evolves over the course of a magnetic storm.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".