Cooperative Suppression of Negative Effects Associated With Multicollinearity and Abnormal Data for Aeromagnetic Compensation
Bibliographic record
Abstract
Compensating the magnetic interference related to the airplane maneuvers is essential to high-precision aeromagnetic measurement. In estimating the compensation coefficients, aeromagnetic compensation based on the Tolles-Lawson model needs to address the reduction in accuracy and robustness arising from several negative effects. Because existing methods only suppress single effects independently, the scope of their application is very limited. To address this problem, a cooperative suppression method of the negative effects associated with multicollinearity and abnormal data is proposed in this study. For this method, abnormal data produced by the dead-zone effect of a classical optically-pumped magnetic sensor are assigned lower weights, thereby reducing their negative influence in the estimation of the compensation coefficient. Moreover, a ridge parameter is added to further reduce the multicollinearity of the compensation coefficient. Theoretical evaluation shows that, in the presence of 20% abnormal data and 0.5 correlation, this method reduces the mean square error for the coefficient to 0.76 compared with 4.39 for the least-squares method, 3.43 for the ridge method, 2.13 for the robust method. For practicality verification, we built an experimental platform and mounted it on an aeromagnetic survey airplane, and then performed a compensation flight test. The results demonstrate an improved ratio for our method of 12.79, which is significantly higher than 0.56 for the least-squares method, 2.73 for the ridge method, 4.29 for the robust method, and 7.95 for the state-of-the-art commercial compensator.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".