Constructing piecewise-constant conductivity models for the McArthur River, Canada, uranium mine audio-magnetotelluric data-set
Bibliographic record
Abstract
The McArthur River uranium mine located in the Athabasca Basin, Saskatchewan, Canada, is one of the largest high-grade uranium mines in the world. The EXTECH-IV project, managed by the federal and provincial governments and by industrial partners, investigated the unconformity-type uranium deposits in the Athabasca Basin, and methods to find them. The audio-magnetotelluric (AMT) data-set was one of the geophysical data-sets acquired in this region during this project. This data-set has subsequently been inverted using a range of 2D and 3D minimum-structure inversion codes — sum-of-squares measures — to model the basement graphitic fault zone associated with the uranium deposit. The constructed conductivity models are in good agreement with each other and show the approximate location and shape of the graphitic fault zone, the "P2" fault, however, they are smeared-out and fuzzy: there is a smooth transition between the conductive anomaly and the background. Hence, we implemented non-L2 measures, in particular the L1 and L0, and the fuzzy c-mean (FCM) clustering approach to invert this data-set for piecewise-constant models with sharp and distinct interfaces. We also compared the capabilities of these three approaches in constructing piecewise-constant models, the sensitivity of the inversion results to a priori information, and the computational cost of the inversion process. These methods that are extensions of the minimum-structure approach retain the capability, robustness, and efficiency of this approach. To invert the data, we discretized the subsurface using an unstructured tetrahedral mesh and included the topography of the study area into the model, unlike the previous studies for which the subsurface was parameterized using rectilinear meshes and the observation points were placed in a flat surface. Instead of rotating the data to align with the trend of the graphitic fault zone, which is in the NE-SW direction, the strike orientation information of the P2 fault was included into the inversion framework. The diagonal and off-diagonal elements of the impedance tensors for a relatively high range of eight frequencies were inverted. The steeply dipping conductive graphitic fault zone in the basement constructed using non-L2 measures and the FCM clustering approach is more piecewise constant and has sharper interfaces compared to the L2 inversion results. The model constructed model using the FCM clustering approach is more localized and distinct compared to the non-L2 inversion results. However, the inversion results using this approach are more sensitive to a priori information provided by the user such as the number of clusters and the cluster center values. Also, this approach introduces more non-linearity into the inverse problem compared to the non-L2 measures, which can make the inversion process computationally expensive. The dependency of the L1 measure on a priori information is less than the L0 measure whereas the obtained models are very similar to each other. Hence, adopting and using one of these three approaches depends on the problem and user preferences. Each of these methods can construct models with sharp, localized, distinct interfaces, which can be more appropriate in many situations than models constructed using the L2 norm. Keywords: Audio-magnetotelluric(AMT), inversion, McArthur uranium mine, piecewise-constant models
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".