Magnetic and Gravity Surface Geometry Inverse Modelling of the TAG Active Mound
Bibliographic record
Abstract
Seafloor massive sulfide deposits form in remote environments, and the assessment of deposit size and composition through drilling is technically challenging and expensive. To aid the evaluation of the resource potential of seafloor massive sulfide deposits, three-dimensional inverse modelling of geophysical potential field data (magnetic and gravity) collected near the seafloor can be carried out to further enhance geologic models interpolated from sparse drilling. Here, we present inverse modelling results of magnetic and gravity data collected from the active mound at the Trans-Atlantic Geotraverse hydrothermal vent field, located at 26 o 08’N on the Mid-Atlantic Ridge, using autonomous underwater vehicle (AUV) and submersible surveying. Both minimum-structure and surface geometry inverse modelling methods were utilized. Through deposit-scale magnetic modelling, the outer extent of a chloritized alteration zone within the basalt host rock below the mound was resolved, providing an indication of the angle of the rising hydrothermal fluid and the depth and volume of seawater/hydrothermal mixing zone. The thickness of the massive sulfide mound was determined by modelling the gravity data, enabling the tonnage of the mound to be estimated at 2.17 +/- 0.44 Mt through this geophysics-based, non-invasive approach.
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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 | 0.000 |
| 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) | 0.001 | 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".