3D vector finite-element electromagnetic forward modeling for large loop sources using a total-field algorithm and unstructured tetrahedral grids
Bibliographic record
Abstract
ABSTRACT Unstructured tetrahedral grids with local refinement facilitate the use of total-field solution approaches to geophysical electromagnetic (EM) forward problems. These approaches, when combined with the vector finite-element (FE) method and with refinement near transmitters and receivers, can give accurate solutions and can easily handle realistic models with complex geometry and topography. We have applied this approach to 3D forward modeling for fixed- and moving-loop configurations. MUMPS, a direct solver, was used to solve the linear system of equations generated by FE analysis. A direct solver is particularly suited to the moving-loop configuration for which the right side is different for every transmitter loop, but for which the coefficient matrix is unchanged. Therefore, the coefficient matrix need only be factorized once, and then the system can be solved efficiently for all different right sides. We compared our results with several typical scenarios from the literature: a conductive brick in a homogeneous half-space and a complex conductor at a vertical contact both for fixed-loop configurations, and a homogeneous half-space for a moving-loop configuration. We also evaluated results for the massive sulfide ore deposit of the Ovoid Zone at Voisey’s Bay, Labrador, Canada, for which we considered fixed- and moving-loop configurations. This model also provides an illustration of the complex vortex current systems that are generated by time-domain EM methods within highly conductive ore bodies in a resistive host.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".