3D parametric hybrid inversion of time-domain airborne electromagnetic data
Bibliographic record
Abstract
ABSTRACT We have developed a method to invert time-domain airborne electromagnetic (AEM) data using a parametric level-set approach combined with a conventional voxel-based technique to form a parametric hybrid inversion. The approach was designed for situations in which a voxel-based inversion alone may struggle. Such an example is where a distinct anomaly is present with sharp boundaries, and there is a large contrast between a low-resistivity target and a high-resistivity background. The first step of the proposed hybrid method used our novel parametric inversion to recover a best-fitting skewed Gaussian ellipsoid that represented the target of interest. Subsequently, the parametric result was set as an initial and reference model for the second stage, where smooth features with smaller resistivity contrasts were introduced into the model through a conventional voxel-based approach. The approach was tested with synthetic and field data. In the synthetic case, we recovered the size and dip of a conductive, thin, dipping plate with better accuracy compared with a voxel-based inversion. In the field example, we inverted AEM data over the Caber volcanogenic massive sulfide deposit. Based on information from past drilling, our results improve upon previous parametric plate inversions of the deposit itself, while additionally imaging the conductive cover over the deposit. These findings showcased how our parametric hybrid method can improve the accuracy of time-domain AEM inversions for thin dipping targets with large resistivity contrasts compared with the background.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".