High‐resolution unmanned aerial vehicle aeromagnetic surveys for mineral exploration targets
Bibliographic record
Abstract
ABSTRACT Recent advancements in geophysical exploration have been realized through reliably integrating unmanned aerial vehicle platforms with lightweight, high‐resolution magnetometer payloads. Unmanned aerial vehicle aeromagnetic surveys can provide a contemporary data product between the two end‐members of coverage and resolution attained using manned airborne and terrestrial magnetic surveys. This new data product is achievable because unmanned aerial vehicle platforms can safely traverse with magnetometer payloads at flight elevations closer to ground targets than manned airborne surveys, while also delivering an increased coverage rate compared to walking conventional terrestrial surveys. This is a promising new development for geophysical and mineral exploration applications, especially in variable terrains. A three‐dimensional unmanned aerial vehicle aeromagnetic survey was conducted within the Shebandowan Greenstone Belt, northwest of Thunder Bay, Ontario, Canada, in July 2017. A series of two‐dimensional grids (∼500 m × 700 m) were flown at approximate elevations of 35, 45 and 70 m above ground level using a Dà‐Jiāng Innovations multi‐rotor unmanned aerial vehicle (S900) and a GEM Systems, Inc., Potassium Vapour Magnetometer (GSMP‐35U). In total, over 48 line‐km of unmanned aerial vehicle aeromagnetic data were flown with a line spacing of 25 m. The collected aeromagnetic data were compared to a regional heliborne aeromagnetic survey flown at an elevation of approximately 85 m above the terrain, with a line spacing of 100 m, as well as a follow‐up terrestrial magnetic survey. The first vertical derivative of the gathered unmanned aerial vehicle total magnetic field data was calculated both directly between each of the different flight elevations, and indirectly by calculating the values predicted using upward continuation. This case study demonstrates that low flight elevation unmanned aerial vehicle aeromagnetic surveys can reliably collect industry standard total magnetic field measurements at an increased resolution when compared to manned airborne magnetic surveys. The enhanced interpretation potential provided by this approach also aided in delineating structural controls and hydrothermal fluid migration pathways (a pair of adjacent shear zones) related to gold mineralization on site. These structural features were not clearly resolved in the regional manned airborne magnetic data alone, further demonstrating the utility of applying high‐resolution unmanned aerial vehicle aeromagnetic surveys to mineral exploration applications. The conclusions and interpretations drawn from the unmanned aerial vehicle aeromagnetic data, coupled with historical data, were applied to make a new gold mineralization discovery on the site, assayed at 15.7 g/t.
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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 | 0.001 | 0.001 |
| 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".