Tensor magnetic gradiometry for mapping complex anomalies
Bibliographic record
Abstract
Complex magnetic targets, such as unexploded ordnances, mine tailings, and firearm caches are more difficult to map with conventional survey methods. Present configurations of survey aircrafts typically utilize total field and vector magnetometers to directly measure large scalar quantities and support aircraft compensation respectively. A more sensitive measurement device, or a combination of sensors, and thorough data processing need to be in place to aid aeromagnetic data users' changing needs to identify these complex targets. Magnetic tensor gradiometry offers an approach to this problem. It provides the magnetic gradient components along nine directions, producing six independent magnetic field gradient values (gxx, gyy, gzz, gxy, gxz, gyz), rather than one singular total magnetic field value or three vector fields. Magnetic gradiometry is typically carried out using a highly sensitive, but costly, superconducting quantum interference device (SQUID). The immediate benefits of tensor magnetics for airborne magnetic surveying are: better resolution of small scale magnetic field variations; magnetic field parameters (e.g. depth to target, target directionality, and magnetization); and mitigation of magnetic interference due aircraft maneuvers. In this study, we carried out airborne and ground magnetics surveys at the National Research Council, Ottawa, Canada in an attempt to compute magnetic tensors without a SQUID, only commercially available magnetometers. Four vector magnetometers were employed in conjunction with a total field magnetometer and data was collected over a variety of stationary targets with prominent magnetic signatures. For the airborne survey, the magnetometers were installed in a horizontal gradient towed-bird in a long line from a single engine helicopter. A similar horizontal configuration on a mobile platform was used for the ground surveys. As there was no vertical offset in the vector magnetometers, only partial magnetic tensors (gxx, gxy, gxz, gyy, gyz) could be computed and the remainder calculated from potential field theory. We show the successful reconstruction of the total field signature of the targets, analysis of the signature that these gradient field values allow, and assess target directionality relative to the sensors and platform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".