Constructing piecewise-constant conductivity models for 3D magnetotelluric inversions
Bibliographic record
Abstract
ABSTRACT The models constructed using the minimum-structure, or Occam’s, style of inversion are smeared-out and fuzzy. These blurred models may be sufficient and appropriate in the early stages of an exploration project and in regions where the subsurface structure is smooth; however, they do not give a realistic representation of thin structures or localized ore bodies that are essentially uniform geologic units separated by sharp interfaces from their surrounding host rocks. To construct piecewise-constant models with sharp interfaces, ℓ1 measure, ℓ0 measure, and fuzzy c-mean (FCM) clustering approaches were implemented and compared for the 3D magnetotelluric (MT) inverse problem using unstructured tetrahedral meshes. These methods were applied to 3D synthetic MT data and real audio-magnetotelluric (AMT) data collected in an exploration project over the McArthur River uranium mine in the Athabasca Basin, Canada, to demonstrate the effectiveness of the approaches. For the real example, geologic orientation information of the structure was also incorporated into the inversion framework. The examples showed that these approaches, in particular the clustering approach, can construct piecewise-constant models with sharp interfaces and clear boundaries between anomalous targets and their surrounding host rocks. The examples also demonstrated that the clustering approach introduced more nonlinearity into an objective function than non-ℓ2 measures, and this approach was more sensitive to the user-provided a priori information for an inversion than non-ℓ2 measures. Including geologic orientation information in the inversion framework improved the quality of the constructed models and allowed AMT data to be inverted in the coordinate system in which they were measured; there was no need to rotate the data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".