A 3D forward-modeling approach for airborne electromagnetic data using a modified spectral-element method
Bibliographic record
Abstract
ABSTRACT The spectral-element (SE) method, which is based on the Galerkin technique, has been gradually implemented in geophysical electromagnetic (EM) 3D simulation. The accuracy and efficiency of this approach, implemented for deformed hexahedral and regular meshes, has been verified for airborne EM forward modeling. One advantage of the SE method over the conventional finite-element method is that it provides accurate results for earth models that can be adequately parameterized using a coarse mesh. However, realistic models can contain important small-scale conductivity variations or larger features with complicated boundaries. To overcome the limitations imposed by using the same mesh to parameterize the model and for implementing the forward-modeling approach, we have developed an adaptation of the conventional SE method. This is inspired by the ideas behind the element-free Galerkin (EFG) method, in which the conductivity is no longer assumed to be constant within a cell; instead, it is handled via the same kind of numerical integration as in the EFG method. This allows a coarse, regular hexahedral mesh to be used for the forward modeling for complex earth models. After presenting the theory for this new SE approach, we test it for airborne EM modeling of 1D and 3D models to verify its flexibility and accuracy. Finally, we model the Ovoid Zone massive sulfide ore body located at Voisey’s Bay, Labrador, Canada, to illustrate the flexibility and practicality of our approach.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".