3D electromagnetic modeling of graphitic faults in the Athabasca Basin using a finite-volume time-domain approach with unstructured grids
Bibliographic record
Abstract
ABSTRACT Uranium exploration in the Athabasca Basin, Canada, relies heavily on ground-based transient electromagnetic (TEM) surveys to target thin, steeply dipping graphitic conductors that are often closely related to the uranium ore deposits. The interpretation of TEM data is important in identifying the locations and trends of conductors to guide subsequent drilling campaigns. We develop a trial-and-error modeling approach and demonstrate its application to the interpretation of a data set acquired at the Close Lake in the Athabasca Basin. The modeling process has two key tasks: building geoelectric models and computing their TEM responses. The modeling process is repeated with the geoelectric model being iteratively refined based on the match between three-component calculated and measured data from early to late times. To create geoelectric models, we first build a realistic geologic model and discretize it using an unstructured tetrahedral mesh, with each mesh cell populated with appropriate resistivities. To calculate the TEM responses of the geoelectric model, we use a 3D finite-volume time-domain algorithm. We construct our initial model based on existing geologic information and drilling data. We find that this modeling process is flexible and can easily handle thin, steeply dipping conductive graphitic fault models with variable resistivities in the fault and background and with topography. Our interpretation of the Close Lake data matches well with the trend and location of the main conductor as revealed by drilling data and also confirms the existence of a smaller conductor that only caused noticeable anomalous responses in early-time horizontal-component data. The smaller conductor was suggested by previous electromagnetic data but was missed in a recent interpretation based on the modeling of only late-time vertical-component data with plate-based approximate modeling methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".