Parallel inversion of large-scale airborne time-domain electromagnetic data with multiple OcTree meshes
Bibliographic record
Abstract
Airborne time-domain electromagnetic surveys are effective tools for mineral exploration and geologic mapping. 3D inversion of airborne electromagnetic data is a challenging computational problem. The size of the surveys and the spatial resolution required to adequately discretize the transmitters and receivers results in very large meshes. Solving the forward problem repeatedly on such a mesh can quickly become impractical. Fortunately, using a single mesh for both the forward and inverse problem for all of the transmitters is not necessary. The forward problem for a single source or a small group of sources can be solved on different meshes, each of which only needs to be locally refined close to the selected transmitters and receivers. Away from the selected transmitters and receivers, the mesh can be coarsened. The forward problem can then be broken into a number of highly parallel problems. Each forward modelling mesh is optimized specifically to the selected transmitters and receivers and has far fewer cells than the fine inversion mesh. Further efficiency can be gained by using stochastic Gauss–Newton methods where a stochastic approximation to the gradient, Hessian or both are used. In this paper, we present new algorithms for airborne data inversion and their implementation using a finite volume discretization on OcTree meshes. We demonstrate our approach on a large-scale synthetic versatile time-domain electromagnetic surveying data set.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".