Airborne Vertical Magnetic Gradient For Uxo Detection
Bibliographic record
Abstract
Oak Ridge National Laboratory (ORNL) and the US Army Engineering and Support Center, Huntsville (USAESCH) have been developing advanced helicopter platforms for magnetometer arrays since 1997. A significant portion of the funding since 1999 has been through the Department of Defense Environmental Security Technology Certification Program (ESTCP). The most recent refinements to the Oak Ridge Airborne Geophysical System (ORAGS) focused on noise reduction techniques. Airborne geophysical systems for UXO detection have been presented by ORNL team members at several SAGEEP conferences (various authors, SAGEEP’95-01). The most recent development in airborne magnetometry includes measured vertical gradient. Gradients in the magnetic field are often used to enhance details or add new insights for interpretation. These can take the form of horizontal gradients, vertical gradients or total gradients, also referred to as analytic signal. There are several fundamental benefits that can be gained through direct measurement over methods that involve calculation from gridded total field maps. In addition to bypassing the filtering and gridding of total field data, three benefits are derived from direct measurement: 1) improved sensitivity to smaller targets, 2) better response at higher altitudes and 3) better resolution of closely spaced targets. In order to implement a vertical gradiometer system, several logistical and engineering problems had to be addressed. These included ground and rotor clearance of the sensor pods, uneven torques applied to the vertical pod structure, vertical and horizontal sensor spacing. The ORAGS-VGrad system was first flight tested in Toronto for stability and airworthiness in December 2001. This paper examines the issues related to the geophysical benefits of measured vertical gradient, and reports on the results of the design and flight testing activities during 2001.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".