Determination of ground conductivity and system parameters for optimal modeling of geomagnetically induced current flow in technological systems
Bibliographic record
Abstract
Abstract In this work, methods to determine technological system parameters and the ground conductivity structure from different sets of geomagnetically induced current (GIC), magnetic field and geoelectric field observations are explored. The goal of the work is to enable optimal modeling of induced currents in any technological system experiencing GIC. As an additional product, the introduced methods can also be applied to utilize GIC observations in the imaging of the subsurface geological structures. Here a robust processing scheme and Occam’s inversion technique familiar from magnetotelluric (MT) studies are applied to the determination of the ground conductivity structure. The application of the methods to GIC data from the Finnish pipeline for a storm period of October 24-November 1, 2003 demonstrate that optimal system parameters and ground conductivity structure can be obtained using time series comprising only 8 days worth of data. Importantly, the obtained ground model is in agreement with models obtained in earlier MT studies. Furthermore, it is shown that although in an ideal case the magnetic field data used should be obtained from the immediate vicinity of the GIC observation site, some spatial separation (200–300 km) between the sites can be tolerated.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".