Modeling Geoelectric Fields Induced by Geomagnetic Disturbances in 3D Subsurface Geology, an Example From Southeastern Australia
Bibliographic record
Abstract
Abstract Geomagnetic storms can cause power grid instabilities and blackouts due to excessive geomagnetically induced currents (GICs) flowing in electric transmission systems. In this study, we assess regional vulnerability to GICs by modeling the geoelectric fields induced by significant historic geomagnetic disturbance events in the presence of 3D subsurface geology using data from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) magnetotelluric array, Australia‐Wide Array of Geomagnetic Stations (AWAGS) magnetometer array, and Geoscience Australia geomagnetic observatory network. We analyze the vertical component of the magnetic field with respect to the horizontal magnetic‐field polarization for two magnetic storms and gain insight into the inductive effects associated with field polarization orientations in the 3D case. We also analyze the telluric field intensity and polarization for a unit geomagnetic field polarized in northerly and easterly directions at AusLAMP sites and find that in the presence of 3D geology the induced field has a very polarization‐sensitive anomaly. We model the geoelectric fields in southeastern Australia for the 1989 “Québec storm.” The induced ground electric fields are typically in the range 1,000–2,000 mV/km with a few sites within 2,000–5,000 mV/km on highly resistive regions and in coastal areas, and below 300 mV/km on inland sedimentary basins. The current study focuses on magnetic‐field variations with periods between 120 and ~20,000 s due to bandwidth limits in our magnetotelluric tensor data and the Nyquist limit for the 60 s sampling of our geomagnetic‐field data. Hence, our modeled maximum values should be considered lower estimates of potential real values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".