3D Multiple Body Parametric Inversion of Time-domain Airborne EM Data
Bibliographic record
Abstract
Summary We developed a 3D parametric inversion for time-domain airborne EM data using a skewed ellipsoid representation for multiple conductive or resistive anomalies. The approach aims to simplify the task of imaging thin, potentially highly conductive, anomalies with 3D EM inversion. The algorithm finds the optimal location, shape, size and resistivity of the anomalies in a homogeneous or heterogeneous background by employing a Gauss-Newton style optimization. Our parametric method is tested on a synthetic and field data set. The synthetic model is composed of two narrow dipping conductive anomalies in a resistive background along with a vertical narrow conductor. The survey layout and resistivity structure is based off field data from a greenstone setting. The parametric inversion accurately recovers the spatial extent and dips of the three synthetic anomalies, although the depth extent of the anomalies is exaggerated. In the greenstone field example, the inversion defines the spatial location, extent and dips of three conductive anomalies to provide a new conductivity interpretation of an area where little information is known regarding the true nature of the conductors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".