Why every electromagnetic inversion needs a 3D forward simulation
Bibliographic record
Abstract
Abstract Modern electromagnetic (EM) surveys deliver high-quality data with broad spatial coverage and high spatial density, providing an opportunity to produce higher-quality images of the earth’s subsurface than in the past. Because geologic structures are inherently 3D, the ultimate goal of an EM survey is to recover a 3D conductivity model that supports robust decision-making and interpretation. Nonetheless, practical inversion and interpretation workflows often still rely on simplifying assumptions, such as a 1D layered earth or parametric targets like plates. This paper illustrates the value of incorporating 3D forward simulation into the interpretation workflow. By running a forward simulation for the EM responses of inversion-derived models in full 3D, practitioners can assess the consistency of inversion results with the measured data and identify potential artifacts. Using two examples, namely, a dipping conductor and a porphyry-style deposit, we demonstrated how models that appear plausible under 1D assumptions fail under 3D scrutiny. Identifying areas where 1D assumptions fail can prompt additional analysis, such as iterative hypothesis testing or more focused 3D inversions. As such, 3D forward simulation should be considered an essential tool in the EM practitioner’s toolbox.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".