Inversion jointe de données électromagnétiques et électriques, pour l’exploration des gisements d’uranium de type discordance du bassin d’Athabasca (Saskatchewan, Canada)
Bibliographic record
Abstract
The Athabasca Basin (Canada) hosts uranium deposits associated with graphitic structures and alteration halos, located at the unconformity between sediments and the metamorphic basement. Electromagnetic (EM) and direct current (DC) electrical methods, which are sensitive to electrical conductivity, are traditionally inverted separately, limiting the distinction between graphitic conductors and alteration. This study evaluates the contribution of joint EM-DC inversion for modeling these features. An initial phase, based on 2D synthetic models, demonstrates that joint inversion reconciles EM and DC datasets and reduces ambiguity regarding the presence of alteration. Application of this method to field data acquired at the Cigar Lake property confirms the value of joint inversion, yielding conductive structures consistent with drilling observations and a model thatreconciles all EM and DC data. We also present an extension of the methodology to 3D inversion, tested on synthetic cases and field data, which highlights the orientations of graphitic conductors. Additionally, using the X and Z components of the EM field—which exhibit the strongest signal—allows for better delineation of conductors.These results contribute to the renewal of exploration methods for uranium deposits in the Athabasca Basin. New perspectives can be considered to maximize the benefits of 3D joint EM-DC inversion, including the design of acquisition devices and the integration of additional datasets sensitive to electrical conductivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".