Commercial operation of a SQUID-based airborne magnetic gradiometer
Bibliographic record
Abstract
Abstract Gradient measurement of the magnetic field vector, especially full-tensor magnetic gradiometers (FTMGs), provides various advantages over gradient components derived from measurements of the total magnetic intensity (TMI). These advantages include higher spatial resolution, directional information, and thus more detailed anomaly delineation and a significantly better-constrained solution space for magnetic inversion and interpretation. However, the airborne application of FTMG instruments requires exceptionally high sensor resolution and low levels of motion noise to maximize these advantages and to achieve high exploration depth. Superconducting quantum interference detectors (SQUIDs) are an effective option for FTMG sensors, now available commercially in the system discussed herein. This SQUID sensor system comprises intrinsic planar-type gradiometers that produce data with sufficiently low noise for use on an airborne platform. The evolution and advancement of the system and its predecessor over the past two decades has produced a robust commercial system that produces high-quality full-tensor data sets from a helicopter-towed-bird implementation. Because the SQUID sensors measure directionally sensitive data, the processing of the acquired data is significantly more challenging than for TMI sensors. Noise induced by the motion of the bird during flight, especially rotational noise, must be monitored and compensated. The introduction of a more robust and aerodynamic bird has significantly reduced the noise of the system. This noise reduction translates into greater sensitivity and accuracy and, thus, heightened confidence in the use of the survey data sets. While much of the early use of the system has been in diamondiferous kimberlite exploration, the system has successfully flown surveys in mineral exploration for a variety of targets, including gold, nickel, and iron ore. These data sets provide greater confidence in the geologic interpretation across the survey areas. Other applications for FTMG surveying include infrastructure mapping, unexploded ordnance detection, and compensation of electromagnetic data sets in marine environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".