Validating airborne vector gravimetry data for resource exploration
Bibliographic record
Abstract
Abstract Airborne gravimeters based on inertial navigation system (INS) technology are capable, in theory, of providing direct observations of the horizontal components of anomalous gravity. However, their accuracy and usefulness in geophysical or geological applications is unknown. Determining the accuracy of airborne horizontal component data is complicated by the lack of ground-surveyed control data. We determine the accuracy of airborne vector gravity data internally using repeatedly flown line data. Multilevel wavelet analyses of raw vector gravity data elucidate the limiting error source for the horizontal components. We demonstrate the usefulness of the airborne horizontal component data by performing Euler deconvolutions on real vector gravity data. The accuracy of the horizontal components is lower than the accuracy of the vertical component. Wavelet analyses of data from a test flight over Alexandria, Ontario, Canada, show that the main source of error limiting the accuracy of the horizontal components is time-dependent platform alignment errors. Euler deconvolutions performed on the Timmins data set show that the horizontal components help in constraining the 3D locations of regional geological features. It is thus concluded that the quality of the airborne horizontal component data is sufficient to motivate their use in resource exploration and geological applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".