Attitude corrections of helicopter EM data using a superposed dipole model
Bibliographic record
Abstract
Abstract The analysis of helicopter-borne electromagnetic (EM) survey data commonly assumes that the EM sensor (bird) has flown straight and levelly, whereupon the coils are either horizontal or vertical. The EM data typically are transformed, under this assumption, to yield the electrical properties (conductivity, permeability, permittivity) of a homogeneous earth, or they are inverted to a horizontally layered earth. In actual fact, the bird exhibits some roll, pitch, and yaw rotation, which will generate changes to the data that we call bird attitude effect. On the basis of a superposed dipole model, we distinguish the geometric part of the bird attitude effect from the inductive part. Theoretical investigation shows that the geometric effect results from the reorientation of the bird's coil axes related to the earth. It is independent of the system frequency and the electrical properties of the earth but dependent only on the bird's attitude. In contrast, the inductive effect results from the coupling change between the coils and the conductive earth. It depends not only on the attitude of the bird but also on the system frequency and the earth's electrical properties. We calculate ratios of the EM responses when the bird is rotated to those when the bird flies straight and levelly. These ratios, reflecting the purely geometric effect of the bird's rotation, are simple trigonometric functions of the rotation angles and thus can be easily incorporated into our processing of the EM data. We show that, in the general case of helicopter EM systems, the majority (> 95%) of the attitude error results from the geometric effect rather than from the inductive effect. Sufficient accuracy is obtained by using the simple geometric technique for attitude correction, without requiring information about the earth's electrical properties. We demonstrate the usefulness of the method, using an example from a test survey.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".