Aeromagnetic gradiometry with UAV, a case study on small iron ore deposit
Why this work is in the frame
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs), commonly known as drones, offer several advantages over traditional piloted aircraft. They are characterized by enhanced safety, cost-effectiveness, and the ability to operate in closer proximity to targeted sources. Consequently, magnetic sensors have been adapted or specifically designed for integration onto UAV platforms. However, existing sensors are burdened by issues such as weight, cost, and high power consumption. These challenges are particularly pronounced when employing aeromagnetic gradiometry, which necessitates simultaneous measurements from at least two sensors. In response to these limitations, we propose the implementation of a cost-effective, lightweight, and low-power magneto-inductive sensor with satisfactory resolution aboard a UAV. To evaluate its efficacy, a survey was conducted over a small iron ore deposit in Western Iran. To validate our approach, we compare the results with those obtained using only one sensor on the drone. This comparative analysis reveals that employing a gradiometry array leads to a pronounced steepening of magnetic anomaly margins. Specifically, the gradient of magnetic measurements on four selected profiles increases to 3.8, 4.6, 9.3, and 10 nT/m when utilizing the proposed magneto-inductive sensor, in contrast to the conventional method of gradient determination through mathematical derivatives in the z-direction. This research contributes to the advancement of efficient and economical methods for mineral exploration using UAV-based magnetic surveying techniques.
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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 | 0.001 |
| 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) | 0.000 | 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 it