The potential of rotary-wing UAV-based magnetic surveys for mineral exploration: A case study from central Sweden
Bibliographic record
Abstract
Abstract Unmanned aerial vehicle (UAV)-based geophysical surveys are attractive for land mineral exploration and are gradually opening extraordinary opportunities in providing high-resolution definition of geologic structures and for direct targeting of mineral deposits. There are, however, challenges such as electromagnetic noise from the UAV, limited load capacity, and short flight times. If these are overcome, there will be a new era in using UAV-based geophysical systems for mineral exploration and for a number of mining-related purposes. In this study, we have tested the potential of rotary-wing UAV systems, given their flight flexibility and robustness for direct targeting of iron-oxide deposits in central Sweden. A walking-mode high-precision Overhauser magnetometer was reassembled so that it could be lifted by the rotary-wing system. Successful backyard tests were performed, but during the real experiment several issues related to high UAV noise level and extreme magnetic field from the mineralization delayed data acquisition. At the end, within nearly three hours and 10 sorties, approximately 20 km-line total-field magnetic data were collected covering an area of about 2 km2. Flight lines were designed perpendicular to the strike of the mineralization to maximize data sampling. Two distinct mineralized zones, magnetite- and hematite-rich and only 50–100 m apart, are notable in the magnetic data due to the fine sampling spacing provided by the UAV survey. Historical low-altitude (30 m above the ground) fixed-wing aeromagnetic data are available from the study area and are compared with the UAV data. Both data sets are consistent in delineating the mineralization, therefore demonstrating the potential of UAV-based surveys for mineral exploration in geologically and logistically challenging areas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".