Faulting and off‐axis submarine massive sulfide accumulation at slow spreading mid‐ocean ridges: A numerical modeling perspective
Bibliographic record
Abstract
Abstract The potential of mining seafloor massive sulfide deposits for metals such as Cu, Zn, and Au is currently debated. One key challenge is to predict where the largest deposits worth mining might form, which in turn requires understanding the pattern of subseafloor hydrothermal mass and energy transport. Numerical models of heat and fluid flow are applied to illustrate the important role of fault zone properties (permeability and width) in controlling mass accumulation at hydrothermal vents at slow spreading ridges. We combine modeled mass‐flow rates, vent temperatures, and vent field dimensions with the known fluid chemistry at the fault‐controlled Logatchev 1 hydrothermal field of the Mid‐Atlantic Ridge. We predict that the 135 kilotons of SMS at this site (estimated by other studies) can have accumulated with a minimum depositional efficiency of 5% in the known duration of hydrothermal venting (58,200 year age of the deposit). In general, the most productive faults must provide an efficient fluid pathway while at the same time limit cooling due to mixing with entrained cold seawater. This balance is best met by faults that are just wide and permeable enough to control a hydrothermal plume rising through the oceanic crust. Model runs with increased basal heat input, mimicking a heat flow contribution from along‐axis, lead to higher mass fluxes and vent temperatures, capable of significantly higher SMS accumulation rates. Nonsteady state conditions, such as the influence of a cooling magmatic intrusion beneath the fault zone, also can temporarily increase the mass flux while sustaining high vent temperatures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".