Hydrogeological modelling applied to mineral exploration
Bibliographic record
Abstract
<p>A multidisciplinary approach combining a groundwater hydrogeochemical survey and a 3D groundwater flow model was applied to unconformity-type U mineralization in the Athabasca Basin (Canada), as a new supplementary guide for uranium exploration. This approach was developed at the McClean Lake Operation site (eastern part of the basin), where several uranium deposits have already been mined and others are not yet mined. The goal of ongoing exploration in this area is to find new deposits in the vicinity of known deposits to facilitate possible future mining.</p><p>Groundwater levels were measured in 60 wells and groundwater sampling was carried out in 31 of these wells, some of these wells are screened in bedrock below the unconformity and others in sandstones above the unconformity. Among these wells, we included 4 wells located near a known ore body (SABRE sector) to better evaluate the potential of our approach to identify the presence of U mineralization.</p><p>The results show that in this study area, the U concentration and saturation index maps are not good indicators of U mineralization as U concentrations are very low for all samples due to the strong reducing conditions. However, 5 of the wells show remarkable geochemical composition: the highest total dissolved solids, high Cl concentration and strong relationships between Cl and concentrations of Na, K, Mg, Ca, Fe as well as Sr and Ba, suggesting that these ions may have come from a common source. Four of these five samples belong to the deposit of the SABRE sector, but the fifth well is located upstream of this region, far from a known ore body. A 3-D groundwater model was developed for the entire basin and the flow path ending at this well screen was traced to its source by reverse particle tracking. In the structure of the groundwater model, graphite-rich fault zones are considered the main geological structures controlling groundwater flows. The up-gradient geochemical plume deciphered from the backflows allows the identification of new exploration targets. This approach appears to be an appropriate method for prioritizing locations for future exploration drilling.</p>
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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 | 0.000 |
| 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".