Large Scale 3D Airborne Electromagnetic Inversion - Recent Technical Improvements
Bibliographic record
Abstract
3D airborne electromagnetic (AEM) inversion has routinely been applied to frequency and time-domain problems over the past few years, however this research field continues to undergo rapid improvements with the implementation of new ideas and faster computational resources. To keep pace with these developments, we have rewritten our 3D AEM inversion software suite to leverage the rapid growth in parallel processing, and to create a flexible inversion framework capable of standard inversion plus many additional types: joint, cooperative or parametric, all on semi-structured octree meshes. Our resulting framework further improves recent key ideas such as the decoupling of forward meshes from the inverse mesh, to allow the forward problem to be easily distributed on separate nodes of a cluster for fast and efficient modelling of the fields.We present two large-scale field examples, one in the frequency domain and one in the time domain. The frequency domain survey demonstrates our ability to recover thin conductors, in this case representing orogenic gold targets, across a large region (40km x 35km). The time domain example focuses on a smaller area within a larger survey area where mapping groundwater resources is the primary goal. Here the fine-scale results are compared to a 1D inversion, and we see a good correlation between the 3D and 1D results due to an approximately 1D layered-earth environment. However we see a removal of 1D artifacts in the neighbourhood of vertical conductors and topographic changes in the 3D result with the added bonus of information between lines in which decisions regarding groundwater management can be made.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".