Marine geophysical exploration for seafloor massive sulfides using unmanned underwater vehicles
Bibliographic record
Abstract
Abstract Seafloor massive sulfide (SMS) deposits contain strategic metals essential for modern technologies, but traditional exploration methods using water column surveys and direct observation provide limited insight into subsurface extent and structure. This study evaluated three geophysical methods for SMS exploration using unmanned underwater vehicles: self-potential (SP), magnetic, and controlled-source electromagnetic (CSEM) techniques. Through 3D numerical modeling and inversion studies based on the Trans-Atlantic Geotraverse field, we demonstrated that SP and magnetic methods offer cost-effective, passive reconnaissance capabilities with limited depth resolution, while CSEM provides superior high-resolution 3D imaging but requires more sophisticated instrumentation. Autonomous underwater vehicles excel in large-scale surveys, whereas remotely operated vehicles enhance signal detection through closer seafloor proximity. Integration of multiple geophysical data sets significantly improved detection accuracy and reduced interpretive uncertainties. As marine sensors and numerical algorithms advance, these integrated geophysical approaches will play increasingly crucial roles in efficient SMS exploration, with benefits far outweighing survey costs.
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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) | 0.000 | 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".