Bibliographic record
Abstract
Magnetotellurics is an electromagnetic geophysical method that has been widely used to study structures in Earth's subsurface. Numerical modeling of magnetotellurics is important for survey design, inversion, geological interpretation and many other aspects of geophysical studies. For example, modeling a subsurface conductive body in terms of its conductivity, geometry and dipping angle would yield substantial information on the phase response and sensitivity in an MT survey. While there are many different modeling techniques, the finite element method is most commonly used. In this effort, we present magnetotelluric models of layered Earth, uplift structures, auroral electrojets, and geomagnetically induced currents in power-line skywires using the commercial finite-element package Comsol Multiphysics. The AC/DC module in Comsol can be used to solve Maxwell's equations in the quasi-static limit for modeling the magnetotelluric response. One of the advantages of Comsol modeling is its Graphical User Interface (GUI), which allows users to solve complex single or multi-physics problems in a meshed domain. The use of Comsol also reduces the need for sophisticated computer coding when solving partial differential equations such as Maxwell's equations. In the effort presented here, we first discuss model validation for layered Earth geometries. We then present two examples of magnetotellurics modeling in impact crater and geomagnetically induced current studies. Numerical results were compared with analytical solutions or benchmark results whenever possible.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".