Factors controlling free thermal convection in faults in sedimentary basins: implications for the formation of zinc–lead mineral deposits
Bibliographic record
Abstract
Abstract Stratiform sediment‐hosted Zn–Pb–Ag mineral deposits constitute about 40% of the Earth's zinc resources ( Allen 2001 ), and in most cases their genesis involves the discharge of basinal brines near or on the seafloor through syndepositional faults ( Sangster 2002 ). From the point of view of base metal exploration, it is therefore essential to identify all possible faults that formerly carried the upwelling ore‐forming solutions during mineralising events. This paper presents a numerical investigation of the relative importance of various physical parameters in controlling fluid discharge, recharge and heat transport in faults. A two‐dimensional, free convection of pure water, hydrogeological model is developed for the McArthur basin in northern Australia based on the surface geology, known stratigraphic and structural relationships and regional geophysical interpretations. Numerical experiments and sensitivity analyses reveal that faults with strong initial heat input, due to depth of penetration or magmatic activity, are the most likely candidates to carry discharge fluids to the sites of metal precipitation. Deeper, wider and more permeable faults are more likely to behave as the fluid discharge pathways, whereas shallow, narrow or less permeable faults act as marine water recharge pathways. Compared with these fault‐related factors, aquifer physical properties are less important in determining fluid flow patterns and the geothermal regime. These results are an important step in understanding hydrothermal fluid flow in sedimentary basins in order to develop effective exploration criteria for the location of stratiform Zn–Pb–Ag deposits.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".