Practical 3D inversion of large airborne time domain electromagnetic data sets
Bibliographic record
Abstract
SummaryIn this paper we show that 3D inversion of large airborne time domain EM data, which is traditionally considered impractical, can be rapidly carried out by using a thoughtful workflow. In our 3D inversion algorithm, the number of cells in the mesh and the number of soundings are two factors that slow down the inversion. Therefore, we develop a strategy of adaptive mesh and sounding refinement to minimize the number of cells and the number of soundings required by the inversion. At the beginning, a coarse mesh and a few soundings are used to quickly build up a large-scale model. Then the mesh is refined and more soundings are added based upon their data misfit. At each iteration of the inversion, a certain number of soundings are randomly selected, and we change the data selection from iteration to iteration. This allows us to down-sample the field data without much loss of information. Once the large-scale model is obtained, we carry out some tile inversions that focus on smaller areas with a locally refined mesh to better resolve the small-scale features. The workflow is demonstrated by a synthetic example with 2121 transmitters that takes about 10 hours to be solved compared to about 150 hours if we had started the inversion on a fine mesh and used all of the transmitters. The methodology of speeding up the inversion by adaptive mesh and data refinement can also be applied to other EM surveys.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".